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https://www.weforum.org/agenda/2020/04/coronavirus-education-global-covid19-online-digital-learning/

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During remote teaching, there is a strong focus on literacy and numeracy skills, however the DfE also recognises that it is still important for professionals to make links between subjects and do more cross-curricular teaching. The DfE plans to integrate aspects of the humanities into English where they can, have greater weekly emphasis on PSHE and go over good learning behaviours in the first weeks of re-opening. ([Withdrawn] Adjusting the curriculum for use in school and at home - Case study - GOV.UK (www.gov.uk))

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To increase student motivation to continue their studies, some schools hold weekly competitions, such as in numeracy, writing or art exhibitions which are celebrated with the rest of the year group at the end of the week. This allows children to look forward to challenges and there are opportunities for breadth and enrichment.

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Influence of COVID-19 confinement on students’ performance in higher education.

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Influence of COVID-19 confinement on students’ performance in higher education.

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T. Gonzalez,

M. A. de la Rubia,

K. P. Hincz,

M. Comas-Lopez,

Laia Subirats,

Santi Fort,

G. M. Sacha

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Abstract

This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autónoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that had their face-to-face activities interrupted because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students' performance. This effect is also significant in activities that did not change their format when performed after the confinement. We find that this effect is significant both in subjects that increased the number of assessment activities and subjects that did not change the student workload. Additionally, an analysis of students' learning strategies before confinement shows that students did not study on a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students' learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students' assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance.

Citation: Gonzalez T, de la Rubia MA, Hincz KP, Comas-Lopez M, Subirats L, Fort S, et al. (2020) Influence of COVID-19 confinement on students' performance in higher education. PLoS ONE 15(10): e0239490. https://doi.org/10.1371/journal.pone.0239490

Editor: Haoran Xie, Lingnan University, HONG KONG

Received: April 28, 2020; Accepted: August 30, 2020; Published: October 9, 2020

Copyright: © 2020 Gonzalez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This research was funded by ADeAPTIVE (Advanced Design of e-Learning Applications Personalizing Teaching to Improve Virtual Education) project with the support of the Erasmus+ programme of the European Union (grant number 2017-1-ES01-KA203-038266). This study was also funded by ACCIÓ, Spain (Pla d'Actuació de Centres Tecnològics 2019) under the project Augmented Workplace. This study was also funded by the Fondo Supera COVID-19 (Project: Development of tools for the assessment in higher education in the COVID-19 confinement).

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The coronavirus COVID-19 outbreak disrupted life around the globe in 2020. As in any other sector, the COVID-19 pandemic affected education in many ways. Government actions have followed a common goal of reducing the spread of coronavirus by introducing measures limiting social contact. Many countries suspended face-to-face teaching and exams as well as placing restrictions on immigration affecting Erasmus students [ 1]. Where possible, traditional classes are being replaced with books and materials taken from school. Various e-learning platforms enable interaction between teachers and students, and, in some cases, national television shows or social media platforms are being used for education. Some education systems announced exceptional holidays to better prepare for this distance-learning scenario.

In terms of the impact of the COVID-19 pandemic on different countries' education systems many differences exist. This lack of homogeneity is caused by such factors as the start and end dates of academic years and the timing of school holidays. While some countries suspended in-person classes from March/April until further notice, others were less restrictive, and universities were only advised to reduce face-to-face teaching and replace it with online solutions wherever practicable. In other cases, depending on the academic calendar, it was possible to postpone the start of the summer semester [ 2].

Fortunately, there is a range of modern tools available to face the challenge of distance learning imposed by the COVID-19 pandemic [ 3]. Using these tools, the modification of contents that were previously taught face-to-face is easily conceivable. There are however other important tasks in the learning process, such as assessment or autonomous learning, that can still be challenging without the direct supervision of teachers.

All these arguments end in a common topic: how to ensure the assessment's adequacy to correctly measure students' progress. Thus, how can teachers compare students' results if they differ from previous years? On one hand, if students achieve higher scores than in previous years, this could be linked with cheating in online exams or with changes in the format of the evaluation tools. On the other hand, lower grades could also be caused by the evaluation format change or be attributable to autonomous learning as a less effective teaching method.

The objective of this article is to reduce the uncertainty in the assessment process in higher education during the COVID-19 pandemic. To achieve this goal, we analyze students' learning strategies before and after confinement. Altogether, our data indicates that autonomous learning in this scenario has increased students' performance and higher scores should be expected. We also discuss the reasons underneath this effect. We present a study that involves more than 450 students enrolled in 3 subjects from different degrees from the Universidad Autónoma de Madrid (Spain) during three academic years, including data obtained in the 2019/2020 academic year, when the restrictions due to the COVID-19 pandemic have been in force.

1.1. Background

E-learning has experienced significant change due to the exponential growth of the internet and information technology [ 4]. New e-learning platforms are being developed for tutors to facilitate assessments and for learners to participate in lectures [ 4, 5]. Both assessment processes and self-evaluation have been proven to benefit from technological advancement. Even courses that solely offer online contents such as Massive Open Online Courses (MOOCs) [ 6, 7] have also become popular. The inclusion of e-Learning tools in higher education implies that a greater amount of information can be analyzed, improving teaching quality [ 8- 10]. In recent years, many studies have been performed analyzing the advantages and challenges of massive data analysis in higher education [ 11]. For example, a study of Gasevic et al. [ 12] indicates that time management tactics had significant correlations with academic performance. Jovanovic et al also demonstrated that assisting students in their management of learning resources is critical for a correct management of their learning strategies in terms of regularity [ 13].

Within few days, the COVID-19 pandemic enhanced the role of remote working, e-learning, video streaming, etc. on a broad scale [ 14]. In [ 15], we can see that the most popular remote collaboration tools are private chat messages, followed by two-participant-calls, multi-person-meetings, and team chat messages. In addition, several recommendations to help teachers in the process of online instruction have appeared [ 16]. Furthermore, mobile learning has become an alternative suitable for some students with fewer technological resources. Regarding the feedback of e-classes given by students, some studies [ 17] point out that students were satisfied with the teacher's way of delivering the lecture and that the main problem was poor internet connection.

Related to autonomous learning, many studies have been performed regarding the concept of self-regulated learning (SRL), in which students are active and responsible for their own learning process [ 18, 19] as well as being knowledgeable, self-aware and able to select their own approach to learning [ 20, 21]. Some studies indicated that SRL significantly affected students' academic achievement and learning performance [ 22- 24]. Researchers indicated that students with strongly developed SRL skills were more likely to be successful both in classrooms [ 25] and online learning [ 26]. These studies and the development of adequate tools for evaluation and self-evaluation of learners have become especially necessary in the COVID-19 pandemic in order to guarantee good performance in e-learning environments [ 27].

Linear tests, which require all students to take the same assessment in terms of the number and order of items during a test session, are among the most common tools used in computer-based testing. Computer adaptive test (CAT), based on item response theory, was formally proposed by Lord in 1980 [ 28- 30] to overcome the shortfalls of the linear test. CAT allows dynamic changes for each test item based on previous answers of the student [ 31]. More advanced CAT platforms use personalization to individual learner´s characteristics by adapting questions and providing tailored feedback [ 32]. Research contains numerous examples of assessment tools that can guide students [ 33- 35] and many advances have also been developed in the theoretical background of CAT [ 36]. In this aspect, advantages offered by CAT go beyond simply providing a snapshot score [ 37], as is the case with linear testing. Some platforms couple the advantages of CAT-specific feedback with multistage adaptive testing [ 38]. The use of CAT is also increasingly being promoted in clinical practice to improve patient quality of life. Over the decades, different systems and approaches based on CAT have been used in the educational space to enhance the learning process [ 39, 40]. Considering the usage of CAT as a learning tool, establishing the knowledge of the learner is crucial for personalizing subsequent question difficulty. CAT does have some negative aspects such as continued test item exposure, which allows learners to memorize the test answers and share them with their peers [ 41, 42]. As a solution to limit test item exposure, a large question bank has been suggested. This solution is unfeasible in most cases, since most of the CAT models already require more items than comparable linear testing [ 43].

1.2. Purpose

The aim of this study is to identify the effect of COVID-19 confinement on students' performance. This main objective leads to the first hypothesis of this study which can be formulated as H1: COVID-19 confinement has a significant effect on students' performance. The confirmation of this hypothesis should be done discarding any potential side effects such as students cheating in their assessment process related to remote learning. Moreover, a further analysis should be done to investigate which factors of COVID-19 confinement are responsible for the change.

A second hypothesis is H2: COVID-19 confinement has a significant effect on the assessment process. The aim of the project was therefore to investigate the following questions:

1. Is there any effect (positive or negative) of the COVID-19 confinement on students' performance?

2. Is it possible to be sure that the COVID-19 confinement is the origin of the different performance (if any)?

3. What are the reasons for the differences (if any) in students' performance?

4. What are the expected effects of the differences in students' performance (if any) in the assessment process?

2. Materials and methods

2.1 Measurement instruments

We have used two online platforms. The first one is e-valUAM [ 44], an online platform that aims to increase the quality of tests by improving the objectivity, robustness, security and relevance of assessment content. e-valUAM implements all the CAT tests described in the following sections. The second online platform used in this study is the Moodle platform provided by the Biochemistry Department from Universidad Autónoma de Madrid, where all the tests that do not use adaptive questions are implemented. Adaptive tests have been used in the subjects "Applied Computing" and "Design of Water Treatment Facilities". Traditional tests have been used in the subject "Metabolism".

2.1.1 CAT theoretical model.

Let us consider a test composed by N Q items. In the most general form, the normalized grade S j obtained by a student in the j-attempt will be a function of the weights of all the questions α and the normalized scores ψ (S j = S j( α, φ)), and can be defined as: (1) where the φ i is defined as (2) where δ is the Kronecker delta, A i the correct answer and R i the student's answer to the i-question. By using this definition, we limit φ i to only two possible values: 1 and 0; φ i = 1 when the student's answer is correct and φ i = 0 when the student gives a wrong value. This definition is valid for both open answer and multiple-choice tests. In the case of multiple-choice test with N R possible answers, φ i can be reduced to consider the random effect. In this case: (3)

Independently of using Eqs 2 or 3, to be sure that S j( α, φ) is normalized (i.e. 0< = S j ( α, φ)< = 1), we must impose the following additional condition on α: (4)

In the context of needing a final grade (FG) between 0 and a certain value M, which typically takes values such as 10 or 100, we just need to rescale the S j( α, φ) value obtained in our model by a factor K, i.e. FG j = K S j( α, φ).

We will now include the option of having questions with an additional parameter L, which will be related to the level of relevance of the question. L is a number that we will assign to all the questions included in the repository of the test (i.e. the pool of questions from where the questions of a j-test will be selected). The concept of relevance can take different significances depending on the context and the opinion of the teachers. In our model, the questions with lower L values will be shown initially to the students, when the students answer correctly a certain number of questions with the lower L value, the system starts proposing questions from the next L value. By defining N L as the number of possible L values, the L value that must be obtained in the k-question of the j-test can be defined as: (5) where trunc means the truncation of the value between brackets. It is worth noting that L k is proportional to the sum of the student's answers to all the previous questions in the test. This fact means that, in our model, the L k depends on the full history of answers given by the student. L k is inversely proportional to N Q, which means that it takes a higher number of correct answers to increase L k. Once L k is defined, a randomly selected question is shown to the student. Another important fact that implies the use of Eq 5 in the adaptive test is that we will never have L k<L k-1. In other words, once a student starts answering questions from a certain L value, they will never go back to the previous one. In Fig 1 we show a simple example of the multiple possible paths that a student can follow when facing a test that uses our model. In the example shown in Fig 1 we have used N Q = 6 and N L = 3. In this very straightforward example, the L value changes every time a student gives 2 correct answers (following diagonal arrows on the diagram). Wrong answers imply a vertical drop on the diagram. In this case, the L value will not change (L k = L k-1). The final student's grade G is shown on the lower part of the diagram. The lowest grade (G = 0) corresponds to a test where the student has failed all the N Q questions. In this case, L = 1 for all the possible j values. The highest grade (G = 6) implies that the student has faced questions from all the possible L values (L = {1,2,3} in Fig 1).

2.1.2 Multiple Answer Test (MA-T).

MA-Ts are used for evaluation and self-evaluation of theoretical contents. In this case, we use a standard format where a single correct answer must be chosen from a pool of possible answers shown by the system. Since students can sometimes answer correctly by a random selection, Eq ( 3) must be used when evaluating the score of any item. The format of the MA-T in our CAT method requires the following elements:

Statement

Correct answer

Rest of possible answers (wrong ones)

Level of the item

In addition, the e-valUAM platform, where the method proposed in this article has been implemented, allows optional use of images or sounds for both the statement and the answers. e-valUAM shows the statement and the possible answers. All other information relating to the CAT method such as the level of the item is hidden from the student. The interface also shows optional feedback information about the performance of the learner in the previous item. Finally, it shows the time remaining to finish the test.

2.1.3 Open Answer Test (OA-T).

To train the contents of numerical problems, an OA-T was developed in which the statements include at least one parameter that will change its value with each execution of the application. This kind of question requires the following elements to be created:

Statement with explicit indication of the modifiable parameter(s).

Minimum and maximum values of each modifiable parameter.

Programming code (Matlab in e-valUAM) that calculates the solution to the problem.

Level of the item.

As with MA-T, there is also a possibility of including multimedia files in the item. However, in this type of question, the multimedia option is only available in the question as the answer is numerical and included by the user in all cases. In this case the interface has a space for entering the numerical answer. In this figure, we also show the feedback of the application when an incorrect previous answer has been introduced.

2.2 Design of the experiment

2.2.3 Study of autonomous learning strategies in the control group.

To answer our research questions, we first set up an experiment that obtains accurate measurements of the autonomous learning activities both in the control and the experimental groups. High accuracy in the measurements of the autonomous learning activities is achieved by using the previously described adaptive tools both in learning and assessment. In our experiment, the evaluation format and the e-learning tools are known to the students from the start, which motivates them to use the tools and perform the tests more consistently. In Fig 2 we show the procedure used in our experiments to measure autonomous learning. There are two kinds of contents in the subjects included in the experiment: theory and numerical problems. In the case of theory, students can use the e-valUAM platform through MA-T. The final evaluation is performed by OA-T. In the case of numerical problems, both learning and evaluation use OA-T. In this case, student motivation for using the platform is even higher because their final scores will be obtained by the same tool they use for their autonomous learning. This system is applied in all the experiments related to analysing the autonomous learning activity.

In the case of "Design of Water Treatment Facilities", an additional longitudinal study has been performed and is shown in Fig 2. This study has been performed in three stages. In the first stage, the self-evaluation material was presented to the students right before the evaluation test. In the second stage, the self-evaluation material was available 3 weeks before. To manage the theory contents, a MA-T with three possible answers to each question and only one correct has been created. In the first stage, with the test made available only one day before the exam, students had at their disposal a MA-T composed of 16 questions and with a time limit of 15 minutes. In the second stage, the time available was increased to 30 minutes and the question number to 18. These changes are related to the nature of the subject contents and do not have a significant impact on the study. The OA-T tests had 6 and 9 questions in the first and second stage, respectively. In order to maintain consistency in the subsequent study, an additional effort has been made to keep the difficulty of the questions as similar as possible.

Stage 3 had no new material added, instead, it used all the material created in stages 1 and 2. Stage 3 corresponded to the time window between the end of classes and the last exam. In this stage, all the material was available from the beginning. The protocol used in this stage only varied from stage 2 in the inclusion of a reward for students who used the application on 3 or more different days. The reward was related to a bonus in the final grades.

2.2.4 Effect of COVID-19 confinement.

Once the correct measure of the autonomous learning is ensured, as explained in the previous section, we must develop an experiment to describe the effect of COVID-19 confinement. This experiment compares results of control and experimental groups in the whole assessment process. There are two stages that must be considered to determine the effect of the COVID-19 confinement. The first one corresponds to the period without confinement in the year 2019/2020 (before March 11), when all the measurable activities are performed in similar conditions for experimental and control groups. The second stage corresponds to the period of COVID-19 confinement (after March 11), where some measurable activities were performed in a different format and statistical differences can be found by comparing experimental and control groups.

In the case of "Applied Computing", students use the e-valUAM application on a continuous basis as it is available from the beginning of the course. They use the same OA-T for training and for their final exam, ensuring often and consistent use of the platform throughout the course. For this reason, in "Applied Computing", data analysis is performed over a continuous self-evaluation process over a single test along the whole academic year. The test performed by the students did not change its format or available questions in the whole three years under study.

In the case of "Metabolism", the confinement started just before the beginning of Part IV of the subject. Therefore, in this second stage, the scheduled in-person lectures from Part IV were recorded on video by the tutor and provided to the students. Afterwards, the students had an opportunity to perform the corresponding on-line continuous assessment activities, just in the same way as they would have done under normal conditions (as these activities were always not face-to-face). As this subject has already had an important number of on-line activities programmed prior to the suspension of face-to-face teaching, it is an extremely valuable tool for analysing the effect of the confinement on students' performance.

In Fig 3, we show the full experiment described in this section. First, the two studies performed in years prior to COVID-19 confinement. In these first studies, we focus on the learning strategies of the students and their response to reward scenarios. Information from these first studies will be useful for later discussion of the reasons for the changes caused by the COVID-19 confinement. The second part of our experiment is related to the effect of COVID-19 confinement, where we compare results of similar assessment activities between control and experimental groups. In Fig 3 we include information about all control and experimental groups in the different interventions.

2.3 Participants

This work involves data that has been collected from 458 students of the Universidad Autónoma de Madrid. This data has been collected in the following manner:

1. It does not involve minors.

2. It has been collected anonymously. Students have been identified by a numerical code, avoiding gathering of any personal information.

3. Students have been informed by the lecturers that some information about their activity could be anonymously collected for statistical purposes. Authors of this study did not receive any objections.

4. The tasks related to this study were completely voluntary and they did not in any form alter students' activities, classes, or the assessment process.

Considering these circumstances, we have received confirmation from the Research Ethics Committee of Universidad Autónoma de Madrid that we do not need to apply for ethics approval from our university since no personal data, minors or potentially hazardous activities were involved in the study.

Teachers involved in the study (coauthors of the present manuscript) who were responsible for the subjects taught also gave consent to carry out the study. We obtained verbal consent from the participants in the study.

Experiments have been performed in the subjects "Applied Computing", "Metabolism" and "Design of Water Treatment Facilities".

2.4 Statistical analysis

2.4.1 Applied computing.

In this subject we have expressed student performance as a 0-10 score and analyzed it across all tests performed in self-evaluation. Those OA-T format tests were available from the beginning of the academic year and students used them on a continuous basis. Data from this subject imply results from an adaptive test. This imply that there are some points in the test where students get stacked and some scores are repeated more often than others. We tested if data followed a Gaussian distribution using a D'Agostino and Pearson omnibus normality test [ 45]. As the data did not pass the normality test, we used non-parametric statistical tests to compare the data from all 3 academic years. Means were compared by a Kruskal-Wallis test [ 46] and, when differences were found, by a Mann-Whitney post hoc test [ 47] to determine which pairs of means were different. All statistical analysis was performed using GraphPad Prism 6 (GraphPad Software, La Jolla, California, USA). Data is presented as mean±SD and statistical significance was set at p<0.05.

2.4.2 Metabolism.

Here, we have analysed 2 variables, the score (0-10) obtained by the students in the different activities and the proportion of students that passed the activity (score≥5), from 2019/2020 and the 2 previous academic years. Firstly, we checked if the results from the previous 2 years (2017/2018 and 2018/2019) were similar, thus allowing them to be compared with results from the present year. To perform the analysis, we tested the score data for normality using a D'Agostino and Pearson omnibus normality test [ 41], later comparing them with an unpaired 2-tailed t-test, if they were normal, or with a non-parametric Mann-Whitney test, if they were not normal. Afterwards, we proceeded similarly to compare the results from the present year with those from the previous 2 years. When data from each activity followed a normal distribution, we compared the 3 means by 1-way ANOVA followed by an unpaired 2-tailed t-test. When the data did not follow a normal distribution, we compared the 3 means with a Kruskal-Wallis test followed by a Mann-Whitney post hoc test. In order to compare the proportion of students that passed the activities (score≥5) in different years, we performed a z-test (after confirming that our data met the central limit theorem). All statistical analysis was performed using GraphPad Prism 6. Data is presented as mean±SD and statistical significance was set at p<0.05.

3. Results

3.1 Autonomous learning strategies in the control group

3.1.1 Continuous working of students in a non-rewarded scenario.

In this section, we analyze students' self-learning strategies in the subject "Applied Computing" in the years that were not influenced by COVID-19 (2017/2018 and 2018/2019). However, since the data is very similar in both courses, we will focus con 2017/2018 for clarity. All statistical results can be also applied to 2018/2019. In both years, theoretical lessons were taught in the classroom and practical lessons in computer laboratories with the teacher physically present. Practical lessons always used the e-valUAM platform and the adaptive test described previously.

In Fig 4A, we show the scores obtained by all 2017/2018 students in all the attempts made with adaptive tests. Attempts are ordered chronologically. A score of -1 in the figure implies that the student did not finish the test and did not obtain a numerical score. Those tests have been included in the figure as a part of student's autonomous work and must be considered as learning time; however, we will exclude them from score data analysis. There are some patterns in the figure that must be considered. First, certain scores repeat more than others (2.5, 5 and 7.5), which corresponds to a jump in question level (L) (level 1: 0-2.5, level 2: 2.5-5, level 3: 5-7.5 and, level 4: 7.5-10), meaning that students were not yet able to answer more difficult questions. Another pattern that can be easily seen in the figure is a region where a huge number of tests had a score of -1 (between 600 and 700 approximately). On those days teachers asked the students to find the most difficult questions in the test. For that reason, students interrupted the tests when they reached those questions.

However, the most interesting pattern, which can be observed in Fig 4A, is the distribution of the tests in time, with a much bigger number of tests done in May, being the last month of the academic year for this subject. Students used the platform 688 times in total in May and only 486 times in the period between February and April. It is worth noting that the academic year for this subject ended with the final exam on May 21. In Fig 4B we show the distribution of attempts between February and May. We found 62 in February, which is a low but reasonable number since students did not yet have enough knowledge to face tests properly. They generally started using the platform in the middle of the month. In March, we noted an increase in the number of attempts followed by a slight decrease in April. This difference can be justified by the Easter holidays (one week) in April. The number of attempts strongly increased in May. Even when counting only the first two weeks of the month, we have more attempts than in previous months (292 from May 1-15). In the period of May 16-21, we have recorded 396 attempts (469 if we count the attempts in the final exam). In the inset of Fig 4B we show the attempt distribution in the last period (May 16-21). As we can see, students used the platform 127 times the day before the exam and 173 times the day of the final exam (95 for studying and 78 in the exam itself). More than 33% of tests were performed in the last 6 days before the final exam (and more that 50% of those tests were performed the day before the exam).

3.1.2 Continuous working of students in a rewarded scenario.

To study the effects of a rewarded scenario, we have developed an experiment in the academic year 2017/2018 for students from the subject "Design of Water Treatment Facilities". We have used 3 stages (fully described in the Materials and methods section): stage 1, where the self-evaluation tests have been made available for 1 day; stage 2, where the self-evaluation tests have been made available for 3 weeks; and stage 3, where self-evaluation tests were available for a longer period with a reward related to their use. Fig 5 shows the attempts distribution in all the stages. Focusing on the MA-T, 13/20 students made more attempts in the first stage than in the second, which is striking considering that in the second stage application was available for longer. Three students have not made any attempts in either of the 2 stages.

As for the group of students who have used the application for more than one day, we noted only 3 individuals working for 3 days or more. Considering that the application was available for 3 weeks in this second stage, these results show the very low willingness of the students to work continuously. Focusing on the OA-T, this effect is even more significant, as only two cases in which students used the application for more than one day were recorded. Increasing student motivation and analyzing the results of a more persistent use of the tool was the objective of stage 3.

In Fig 5 we can see the distribution of attempts made by the students in the whole study (3 stages). There is a fourth stage in the figure that corresponds to an additional exam taken by the students that did not pass the previous ones. Since only a few of them were involved in this fourth stage, it was excluded from the analysis. As we can see in the figure, attempts from stage 1 were limited to a couple of days, which corresponds to the short period when the test was available. Some attempts can be found in a wider window in stage 2. However, a much higher number of attempts extended in time can be found in stage 3, which corresponds to the rewarded stage.

In order to measure the use of the tool over time, we summarized the use of the system by students in stage 2 and 3 by comparing the number of days the students attempted the stage 2 MA-T and OA-T tests during the 3 weeks leading up to the exams given after each stage. It is interesting to note that, during stage 3, six more students utilized the system than in stage 2; however, the total attempts reduced between the 2 stages from 123 to 102. This translates to a 23% increase in the average use of the application over time from 1.9 days in stage 2 to 2.4 days in stage 3 combined with a reduction of attempts per student from 9.5 in stage 2, to 5.4 in stage 3. In the earlier stage, fewer students used the application intensively by making multiple attempts a day. Students who logged in at the later stage were more likely to use the application once or twice a day over several days because of the reward related to this behavior. Out of a total sample of 19 students who used the tool in stage 3, 5 were eliminated by the test as they accessed the tests the same number of days in both stages. Then, for the use of the application by the remaining 14 students the W-value was of 15.5, with the critical value of W = 25 at p.0.05. Therefore, the result is significant.

3.2 Effect of COVID-19 confinement

We have studied two cases related to two different and common scenarios in COVID-19 distance learning. The first one is related to subjects where teachers increased the number of tasks that students should perform autonomously. The second one is related to subjects where all the autonomous tasks were the same as in previous courses. In this case, only face-to-face sessions were replaced by distance learning activities such as online sessions or recorded classes.

3.2.1 Additional autonomous activities during confinement.

In Fig 6 we show the scores obtained by all the students in the 3 courses analysed in all the e-valUAM tests performed in the subject "Applied Computing". In the figure we indicate the period of the COVID-19 confinement during the 2019/2020 course (from March 11 to March 30). For clarity, we also indicate the equivalent period in the two previous years. As we can see in the figure, the number of tests performed before confinement was similar (225 and 246) in the two previous years (control group), but there is a remarkable increment in the number of tests (328) performed in the experimental group. These differences can be easily explained normalizing these numbers to the number of students each year (97, 73 and 91 respectively). By doing that, we obtain 2.31, 3.37 and 3.6 test/student, respectively. Thus, differences between courses 2018/2019 and 2019/2020 are reduced, although there is still some difference with respect students from course 2017/2018, that can be explained by the fact that some of these students were retaking the subject and, therefore, did not use the e-valUAM application often until the contents were more difficult. If we count the number of tests finished until the end of the academic year in courses 2017/2018 and 2018/2019, we find 1345 and 1175 tests respectively. Normalizing with the number of students, we find 13.87 and 16.1 test/student respectively. This is a difference of 2.23 test/student in a period of 3.5 months, which means a difference of less than 1 test/student each month.

The conditions after the COVID-19 confinement radically changed in the course 2019/2020 because students were required to perform a higher number of tests each week. As we can see in Fig 6, there are still small differences between courses 2017/2018 and 2018/2019. However, the number of tests taken in 2019/2020 is almost 5 times higher. In the inset of Fig 6 we show a histogram with the data for course 2019/2020. Adaptive tests used in this experiment induce the effect of higher number of attempts at scores 2.5, 5 and 7.5, which are the points where the level of the questions increases. For that reason, the curve is not normal. This fact is statistically tested in the following analysis.

When comparing the mean scores from the period before confinement ( Fig 7), we found that they were statistically different between the 3 academic years. Differences between the mean score from the 2019/2020 (experimental group) and both the other 2 years (control group) were: 4.5±1.6 in experimental group, vs. 3.9±1.5 in 2017/2018, p = 0.0003, and vs. 3.9±1.5 in 2018/2019, p = <0.002), with no differences between 2017/2018 and 2018/2019 (p = 0.997). Similarly, in the period between the beginning of confinement and March 30, there were statistically significant differences between the mean of the present course 2019/2020 and the previous 2 years (6.3±1.6 in experimental group, vs. 5.6±1.8 in 2017/2018, p = 0.0168, and vs. 5.2±2.1 in 2018/2019, p = 0.0002), with no differences between 2017/2018 and 2018/2019 (p = 0.4451). The mean scores are significantly different both in the control and in the experimental groups between both periods (before and after confinement). We have also seen an increase in the mean score differences after the confinement.

3.2.2 Not additional autonomous activities in confinement.

This study has been performed with students from the subject "Metabolism", which is a subject where no additional tasks have been imposed to students because of the COVID-19 confinement. We have analysed students' scores and the proportion of students that passed the activity (score≥5). As it can be observed in Fig 8A, the scores obtained by students in the different activities during the course were consistently similar in both years of the control group, with the only exception in the second activity where there was a slightly decrease in year 2018/2019 (p<0.05). Similarly, the proportion of students with a score≥5 was very similar in the control group (p>0.05) ( Fig 8B). Therefore, students' performance in the previous 2 years (control group) was not significantly different, allowing us to compare these results to the results from 2019/2020. Scores obtained by the students in the 2019/2020 academic year before confinement were similar to those obtained by students from the control group, although some differences were found at the beginning of the course (activities number 2, 3 and 4, p<0.05). We think that this could be due to the adaptation of the students to the course and to the new assessment methodology, being afterwards similar again. However, after the end of face-to-face teaching and at the beginning of the confinement, students' scores were significantly higher than in the previous academic years ( Fig 8A). The most remarkable differences are evident in activities 10 and 11, that have always been on-line activities. Thus, in activity 10, mean score in the present year (2019/2020) was 8.1±0.2 vs. 6.5±0.2 (p<0.0001) and vs. 6.7±0.3 (p = 0.0005) in 2017/2018 and 2018/2019, respectively. Similarly, in activity 11, the mean score of the experimental group was 7.8±0.2 vs. 6.8±0.2 (p = 0.0014) and vs. 6.1±0.3 (p<0.0001) in 2017-2018 and 2018/2019, respectively. Better performance of students was strongly evident when analysing the proportion of students that passed these 2 activities ( Fig 8B). Thus, 95.6% of students passed activity 10 (p<0.0047 vs. 2017/2018 and p = 0.0025 vs. 2018/2019) and 97.7% activity 11 (p<0.0068 vs. 2017/2018 and p = 0.0111 vs. 2018/2019). Moreover, we also found higher scores and an increase in the number of students that passed the activity number 9, which is a test ( Fig 8A and 8B). In the present academic course (2019/2020), this test has been performed by the students confined to their homes, in contrast to previous years where the test was performed in the classroom under the supervision of the tutor. In addition, we have analysed the proportion of students that performed the activities in different courses ( Fig 8C), finding that this proportion is very similar, with a difference in only one activity.

4. Discussion and conclusions

The aim of this study was to identify the effect of COVID-19 confinement on students' performance. Therefore, we conducted an experiment among 450 students from three subjects in different degrees of higher education at Universidad Autónoma de Madrid, Spain. The results of this study answer our 4 research questions:

1. Is there any effect (positive or negative) of COVID-19 confinement on students' performance?

2. Is it possible to be sure that COVID-19 confinement is the origin of the different performance (if any)?

3. What are the reasons of the differences (if any) in students' performance?

4. What are the expected effects of the differences in students' performance (if any) in the assessment process?

With respect to research question 1, the results analyzed in section 3.2 show that there is a significant positive effect of COVID-19 confinement on students' performance. The results indicate that students obtained better scores in all kinds of tests that were performed after the beginning of confinement. Different sources of error have been removed from our study by including only subjects that fit the following requirements for the last three academic years:

Same teaching methodology and teachers in all the years.

Same assessment process in all years, including both distance and on-site activities.

Tests performed both before and after the COVID-19 confinement in the present academic year.

Tests that only imply an objective assessment process in order to avoid any possible subjective interpretation by the lecturers.

We have compared results of students from the present 2019/2020 academic year (experimental group) with results of students from the last two academic years 2018/2019 and 2017/2018 (control group). Students from the two previous academic years had similar circumstances from the beginning to the end of their course. When compared to students of the 2019/2020 academic year, we have two periods with different conditions. The first period is taught in the same conditions as in previous years (before confinement). In the second period, those conditions dramatically changed, and all the teaching and learning activities were limited to distance learning. The results of our studies (section 3.2) clearly indicate that there are significant differences when comparing students' performance in confinement with the performance in previous periods where activities were not limited to distant learning.

At this point, it is clear that the variable that correlates with the change in students' performance is the beginning of the confinement. However, we cannot yet establish if the difference is due to either:

The new learning methodology, or

The new assessment process.

For those reasons, we should now answer research question 2 and establish whether the differences found when answering research question 1 are only due to the COVID-19 confinement.

The problem with confinement is that not only the learning, and teaching strategies should be modified, but also the assessment process as it cannot be done face-to-face. There are some concerns related to these new assessment methods such as the opportunity for cheating by the students. This is the reason why we have chosen only subjects that include several tests that have not been modified because of the confinement. The results of our study show that students also achieved significant improvements in their scores even in tests that were performed in the on-line format in previous years. Moreover, this improvement is only significant when comparing data after the confinement (i.e. there are no significant differences in on-line tests that were performed before the confinement). These findings reveal that the new assessment process cannot be the reason for the improvement in students' performance because the learners also achieved better scores when the format of the assessment did not change. For these reasons, we establish that the new learning methodology is the main reason for the change in students' performance during the confinement.

Now, we shall discuss research question 3 (What are the reasons of the differences (if any) in students' performance?). We have proven that something has changed in students' learning methodology. The question is: What is the common element in those methodologies that caused the improvement in the learning process?

We have analyzed data from two different subjects that used two very different learning strategies during the confinement (section 3.2). In one of them (section 3.2.1), additional e-learning tasks were imposed on the students. Theoretical lessons were replaced by written documents. In the other one (section 3.2.2), no additional e-learning tasks were given, but theoretical lessons were replaced by multimedia classes. In both cases, we have found a significant increment in students' performance in the evaluation tests after the confinement. It seems that students' performance is increased independently of the learning strategies followed by teachers. Since we have established that the assessment process cannot be responsible for the differences, and we cannot find any common elements in the learning methodologies, we must conclude on a general change in the autonomous learning process.

We have also analyzed data from the previous years in two subjects that demonstrate that students do not work on a continuous basis (section 3.1). In both subjects, students work hard only in the last days before the final exams. In one subject, we have found that more than 33% of the autonomous work was done in the 5 days immediately preceding the final exam (section 3.1.1). In the other subject, students only used the e-learning material in the last 2 days before the final exam, even when it had been provided three weeks in advance (section 3.1.2). In this study, we have also found that students easily change their learning behavior and study continuously when a reward is offered. This extra motivation dramatically changed their learning strategy and students worked in a much wider time window, increasing their performance. An increase in the students' performance due to adequate time management in the learning process is well-established in the literature [ 12, 13].

In the present COVID-19 confinement, students can find by themselves many different motivations (rewards) to work on a continuous basis. First, the confinement is a new scenario that has never been faced by the students. For this reason, students do not have any previous experience to use as a reference in their learning process. Without any previous reference, students need to be confident that they are following the course correctly and therefore, work continuously in order not to miss any important content. Another interpretation is that they are afraid of missing the academic year because of the COVID-19 confinement and they work harder to overcome any difficulty. Finally, students might be motivated by their intrinsic responsibility in a very confused situation and work hard to contribute as much as they can to solve the problems that higher education is facing. Most probably, different students will find different motivations in this new scenario (probably a combination of many). We conclude that there is a real and measurable improvement in the students' learning performance that we believe can guarantee good progress this academic year despite the COVID-19 confinement.

Answering question 4, we have demonstrated that students get better grades in activities that did not change their format after the COVID-19 confinement. Moreover, we have demonstrated that there is an improvement in their learning performance. In conclusion, higher scores are expected due to the COVID-19 confinement that can be directly related to a real improvement in students' learning.

Acknowledgments

The authors want to thank Dr. Gema Perez-Chacón for her valuable comments on statistical analysis.

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Influence of COVID-19 confinement on students' performance in higher education

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This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autónoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups.

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The likely impact of COVID-19 on education: Reflections based on the existing literature and recent international datasets.

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The likely impact of COVID-19 on education: Reflections based on the existing literature and recent international datasets.

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Eg. Behaviorism defines learning as observable behavioral change that occurs in response to environmental stimuli.
Is this possible learning from home?

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Psychology.org | Psychology's Comprehensive Online Resource

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Educational psychologists study learners and learning contexts - both within and beyond traditional classrooms - and evaluate ways in which factors such as age, culture, gender, and physical and social environments influence human learning. They leverage educational theory and practice based on the latest research related to human development to understand the emotional, cognitive, and social aspects of human learning.

Educational psychology can influence programs, curricula, and lesson development, as well as classroom management approaches. For example, educators can use concepts from educational psychology to understand and address the ways rapidly changing technologies both help and harm their students' learning. In addition, educational psychologists play an important role in educating teachers, parents or guardians, and administrators about best practices for learners who struggle with conventional education methods.

As psychologists, these professionals often work directly with children - and in collaboration with parents or guardians and teachers - to improve a child's learning outcomes. However, educational psychologists can also pursue careers as researchers, consultants, and teachers in a variety of contexts, including schools, community organizations, government research centers, and learning centers.

Key Theories in Educational Psychology

Although educational psychology programs include numerous theories, many experts identify five main schools of thought: behaviorism, cognitivism, constructivism, experientialism, and social contextual learning theories. The following summarizes these five major theory groups and outlines the key theorists, definition, history, principles, and applications for each.

To expand on these theory groups, we spoke with Aimee Maxwell, Ph.D., a licensed psychologist who provides further insight into these concepts.

Behaviorism

Key Theorists: Edward Thorndike, Ivan Pavlov, John B. Watson, and B.F. Skinner

Definition and Background

Behaviorist learning theories first emerged in the late 19th century from the work of Edward Thorndike and Ivan Pavlov. They were popularized during the first half of the 20th century through the work of John B. Watson, B.F. Skinner, and others.

Behaviorism defines learning as observable behavioral change that occurs in response to environmental stimuli. Maxwell explains, "Behaviorism suggests we do what we do because of what happens around us environmentally, not because of what's inside us innately."

Positive stimuli - or "rewards" - create positive associations between the reward and a given behavior; these associations prompt one to repeat that behavior. Meanwhile, negative stimuli - or "punishments" - discourage the behaviors associated with those stimuli. Through this process of conditioning, people learn to either repeat or avoid behaviors. Maxwell adds, "A bit blank slate-ish, behaviorism suggests that we learn because we get rewards and punishments that make us approach or avoid ways of being."

Because early behaviorists tried to legitimize psychology as a science, their theories emphasized external, scientifically measurable behavioral changes in response to similarly measurable stimuli.

Although they admit that thought and emotion influence learning, behaviorists either dismiss these factors as phenomena beyond the realm of scientific inquiry (methodological behaviorism) or convert internal factors into behavioral terms (neobehaviorism/radical behaviorism).

Assuming that changes in behavior signify learning, methodological behaviorists see no fundamental difference between human and animal learning processes, and they often conduct comparative research on animals.

Key Principle(s)

Behaviorism relies on the prediction or analysis of behavior based on causal stimuli, while education uses the process of positive and negative reinforcement to encourage or discourage behaviors. This school of thought emphasizes behavior's learned causes over its biological one; therefore, behaviorism deeply values the ability of education to shape individuals.

Behaviorist learning theory distinguishes between classical and operative conditioning. The former involves natural responses to environmental stimuli, while the latter involves the reinforcement of a response to stimuli. Using a process often called "programmatic instruction," educators use operative conditioning to reinforce positive and correct negative learnings that often accompany classical conditioning.

Behaviorist theories ascribe to a reductionist approach, which dictates that breaking behavior down into parts is the best way to understand it. Other schools of thought critique behaviorism for underemphasizing biological and unconscious factors, denying free will, equating humans with animals, and overlooking internal learning processes or types of learning that occur without reinforcement.

Application

Behaviorism has significantly shaped the disciplines of psychology and education, illuminating major influencing factors in human behavior and learning. In psychology, both behavior modification and behavior therapy owe their origins to behaviorism.

Meanwhile, behaviorist insights underlie many of the teaching methods still used today in homes, classrooms, workplaces, and other contexts. The widespread use of learning objectives, for example, breaks down larger learning goals into a series of specific skills and behaviors desired from a student.

Behaviorism also influences the sequence and methods used during the teaching and learning process. Teachers work toward their desired objectives by using external stimuli, explaining and demonstrating a skill or behavior, and then inviting student practice and providing feedback that reinforces the behaviors or skills they wish students to learn or unlearn.

Maxwell says, "The idea of measuring behaviors to assess acquired learning is deeply embedded through most educational systems, as is a system of rules with punishments and rewards designed to corral a learner into knowledge."

Cognitivism

Key Theorists: Jean Piaget, Jerome Bruner, Robert Mills Gagne, Marriner David Merill, Charles Reigeluth, and Roger Schank

Definition and Background

Cognitive psychology emerged in the 1950s and became dominant in the 1960s. Departing from the comparative emphasis of behaviorists, cognitivists see human beings as rational creatures quite different from animals. Consequently, cognitive theory explores the complexities of the human mind as it processes information.

It views behavior as a result of one's thoughts. Maxwell states that cognitivists "try to understand how we learn, think, and behave by looking at how our mind works to process, integrate, perceive, decide, emote, and understand existence. Cognitivism helps us see how we use our previous knowledge and experiences to learn new things and develop."

Using the computer as a metaphor for the human mind, cognitivists see learning as a product of mental faculties and activities, including thought, knowledge, memory, motivation, reflection, and problem-solving. Recasting learning as the acquisition of knowledge and the development of understanding, this approach emphasizes reading and lecture as learning modalities.

Rather than measuring learning based on observable behaviors, cognitivists evaluate learning based on a learner's demonstration of knowledge and understanding.

Maxwell says, "Like all good structures, they've come up with some useful rules of thumb for optimized learning. Here's four of them: 1. Make sure the learning has a relevant purpose. 2. Ensure that the learner is focused. 3. Encourage the learner to move towards their own goals. 4. Arrange teaching to foster discovery of ideas and support the learner in slotting those new ideas into whole-mind understanding, at their own pace."

Key Principle(s)

Cognitive psychology understands knowledge acquisition schematically and symbolically. It posits learning as the process of changing a learner's mental model or schematic understanding of knowledge.

In this view, human behavior reflects internal processing of the human mind, rather than simply a conditioned response to external stimuli. Learning involves the integration of information into a stored and usable body of knowledge.

Cognitive psychology derives, in part, from Jean Piaget's stages of development, which depend on biological factors such as age. Learning capacity and activity change over time as a person moves through life. For example, although older people have accumulated more knowledge, they do not always remain as teachable due to their tendency to adopt a more fixed outlook over time.

Cognitivism emphasizes the importance of an expert in transmitting accurate information, yet sees a learner's success or failure in absorbing this information as largely dependent upon the learner's mental capacity, motivation, beliefs, and effort.

Application

The setup of many learning experiences today reflects persistent cognitivist ideas, approaches, and assumptions.

Although many contemporary educational psychologists see cognitivist approaches as outdated, teachers often deliver lectures in front of a classroom and expect students to demonstrate their retention of content through information-oriented tests.

However, teachers' efforts to balance lectures with activities that encourage mental processing also reflect cognitivist influence. Self-reflection - a widely used cognitivist technique - helps students think about and transform their understanding of the subject at hand.

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Constructivism

Key Theorists: John Dewey, Jean Piaget, Lev Vygotsky, and Jerome Bruner

Definition and Background

Constructivism gained notoriety in the 1930s-40s and enjoyed a resurgence in the 1970s-80s. This view challenges both the behaviorist notion of the learner as a blank slate and the cognitivist notion of learning as the acquisition of objective information from an expert.

Rather, this school of thought suggests that learners create their own subjective information by interpreting their world and restructuring their thinking. For instance, "meaning is co-created, emergently between people, and between people and media, morals, ethics, societal norms, and eras," says Maxwell.

Constructivist theories take a learner-centered approach, in which the teacher serves as a guide - rather than the source of - the student's learning.

Maxwell says, "Learners don't just passively receive knowledge from teachers, they generate their own learning by using their experiences and social interactions in combination with teachings."

Originating in part from Piaget's understanding of intellectual growth as occurring through the interaction between old and new knowledge, constructivism views knowledge acquisition as a process of building upon a learner's previous knowledge.

Key Principle(s)

Constructivists agree that learners create knowledge rather than passively receiving it, and that preexisting knowledge plays a crucial role in their learning. However, two differing strands of constructivism bear mentioning.

Social constructivism - associated with Lev Vygotsky's emphasis on social context - posits that students learn naturally through a process of discovery. While late 20th century cognitivist theories tend to reduce a learner to a passive receptacle, social constructivism believes learners actively hypothesize about their environment and test these hypotheses through social negotiations.

Cognitive constructivism agrees that learners construct rather than receive information, but it is interested in the cognitive processing involved in knowledge construction. "Your perspectives and ideas about the world aren't just what you've picked up, like an empty tank slowly being filled; but rather you're building your own house of knowledge and what you explicitly learn is only some of the bricks," Maxwell says.

Following Piaget, cognitive constructivism acknowledges age-based developmental learning stages and articulates learning as an expansion (through assimilation and accommodation processes) of a learner's experientially informed mental model of their world.

Application

Constructivism influences the lesson plan methodologies employed by many teachers today. For example, constructivist influence shapes the common teaching practice of posing questions or problems and then inviting students to answer and solve them in their own ways.

Constructivism is also evident in popular classroom practices, such as having students create their own questions, welcoming multiple points of view and intelligence styles, and using group work as a collaborative learning tool.

Experientialism

Key Theorists: David A. Kolb and Carl Rogers

Definition and Background

This school of thought emerged in the 1970s out of the influence of the learner-centered and interactive principles of constructivism and social learning theories. Experiential learning theories identify meaningful everyday experience as the most central factor in increasing a learner's knowledge and understanding, as well as transforming their behavior.

Namely, "experientialism really drills into the importance of direct experience in the learning process," Maxwell explains.

Experientialist theorist Carl Rogers prioritizes experiential approaches to education because they work with humans' natural desire to learn. Rogers posits that humans are more likely to learn and retain information when they participate actively in the learning process.

Maxwell says, "In educational psychology, experientialism is applied when teachers get students to work on real-world problems through project-based learning, or when students are involved in immersive activities that engage more than just our thinking-brains, e.g., flight simulators, school kitchens or workshops for tech classes, and role-playing exercises to practice dealing with situations before really being in them like debating."

She continues, "These activities allow students to experiment, make mistakes, and develop emergent understanding in the process."

Experientialist David A. Kolb identifies four stages in this learning process: experiencing, absorbing and reflecting on experience, conceptualizing experience, and testing concepts in other situations. These are cyclical stages that function as an ongoing feedback loop, which in turn allows learners to improve skills and apply new or recent knowledge.

Key Principle(s)

Rejecting instructor-centric approaches, experientialism argues that one person cannot effectively impart knowledge directly to another person; people must learn for themselves. A teacher can facilitate the learning process by engaging students through an experience, but they cannot control exactly what students learn from that experience.

"By ensuring direct [in real life] experiences, experientialism helps make learning more engaging, relevant, and memorable!" Maxwell says.

Experientialists argue that learners become less receptive when they are afraid; as a result, this view encourages teachers to create nonthreatening learning environments where learners can experience and experiment freely.

Contemporary experientialists are interested in how a learner's engagement and testing of new skills or concepts influences their learning environment, which creates a larger feedback loop that shapes the world in which we live.

Application

The experientialist understanding of the learning process as a dynamic feedback loop often shapes how educators plan their lessons.

By placing an emphasis on activities that prompt effective perception and processing, educators can activate the learner's prior experience, demonstrate a new skill for the learner, ask the learner to practice the skill, and then invite application of those skills in practical scenarios.

Experientialism also shapes theories of organizational learning, including workplace design and professional training. Such programming often introduces realistic problems or scenarios where professionals practice new skills to generate a constructive solution. Individuals may also work collaboratively and receive feedback from their peers and instructors.

Many schools incorporate experiential education in their programs and curricula. In K-12 schools, these experiences often take the form of field trips or projects.

Meanwhile, colleges offer undergraduates internships and study abroad programs, and graduate schools often incorporate practicum experiences that allow students to apply what they have learned in other courses.

Social and Contextual

Key Theorists: Lev Vygotsky, Albert Bandura, Jean Lave, Barbara Rogoff, Etienne Wenger, and Thomas Sergiovanni

Definition and Background

First emerging in the late 20th century, social and contextual learning theories challenge the individual-focused approaches evident in both constructivism and cognitivism. Social and contextual theories are influenced by anthropological and ethnographic research and emphasize the ways environment and social contexts shape one's learning.

In this view, cognition and learning are understood as interactions between the individual and a situation; knowledge is situated in - and a product of - the activity, context, and culture in which it is developed and used. This led to new metaphors for learning as a "participation" and "social negotiation."

Maxwell adds, "It's a fascinating branch of psychology that studies how social and cultural factors influence our behavior, thoughts, and emotions. Basically, social and contextual psychology looks at how the meaning of things, like words, emotions, and events, depend on the context they occur in."

She says, "It also explores how we learn by observing others and being part of a group, and how that can affect our development. This can include working with colleagues who have different levels of experience, and how we can all stimulate each other's growth."

Social learning theory pays particular attention to social and interactive aspects of learning. Albert Bandura, for example, emphasizes the roles that social observation and modeling play in learning, while Jean Lave and Etienne Wenger put forward that learning works best in a community that produces social capital. They believe this improves the health of the community and its members.

Key Principle(s)

The situated, relational nature of knowledge and the social, engaged nature of effective learning are the foundational principles of social and contextual learning theories.

Bandura posits a reciprocal determinism between environment, personality, and behavior, arguing that these factors influence one another while also shaping learning situations. Emphasizing learner attention, motivation, and memory, Bandura encourages educators to use natural tendencies toward observation, modeling, and imitation when designing learning situations.

Bandura's ideas correlate with Lev Vygotsky's Zone of Proximal Development, where the zone of development is only accessible to a learner through interaction with mentors or other more knowledgeable persons.

Lave and Wenger, on the other hand, view the diversity in knowledge levels as the best asset to a community of practice. They believe that learning depends on a group's effective use of cooperation, trust, understanding, and problem-solving to produce beneficial learning outcomes for the community.

Thomas Sergiovanni seconds this view, arguing that schools and other communities need to shift toward this approach before they can see substantial improvement.

Application

The efforts of today's teachers to connect students' new and preexisting knowledge aligns with social and contextual learning. As a result, teachers account for the demographics of their classrooms as much as they do lesson planning.

Social and contextual learning theories also inform educators' efforts to connect new concepts with direct applications of concepts in specific contexts where a student lives, works, and/or learns.

While educators used to expect learners to make connections on their own, teachers now achieve more successful learning outcomes when they create learning environments that facilitate this process. Many teachers try to incorporate multifaceted, experiential learning environments that assist students in forging meaningful connections between abstract and practical concepts.

A teacher's effort to explicitly address the importance of lesson material reflects the impact of social and contextual learning theory. Explanations and illustrations of reasons for a lesson typically improve student motivation, helping students visualize or actually practice using this knowledge in practical contexts.

"Some examples of social and contextual psychology include social perception, social cognition, social influence, social identity, social interaction, and social change. It's amazing to see how these factors can impact our daily lives and shape our understanding of the world around us," Maxwell states.

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Page last reviewed March 13, 2023

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Introduction To Educational Psychology Theory | Psychology.org

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Educational psychologists study learners and learning contexts. Explore the five major theory groups with information on the key theorists, definition, history, principles, and applications.

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Due to the transition from the classroom to the home environment, students need to feel more supported than ever before. This ensures that the children do not lose motivation in their studies which could hinder their future academic achievement.

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Teaching and Learning, for staff and children.

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Our academic staff have played an integral role in researching what impact COVID-19 has had on society, to help the recovery process and see what lessons can be learned from the pandemic.

Due to the impact of COVID-19 on a national and international scale, our academics repurposed their research activity, and provided scientific support to local and central government as they tried to mitigate the consequences of the virus.

A lot of the work within our school was undertaken via the District's Gold Command COVID-19 Scientific Advisory Group (CSAG). Gold Command is a strategic executive level group that local government initiates for large-scale emergencies. Gold command liaise with senior officers from across government agencies (police, health etc), to guide the strategic response of Silver command (the tactical level) who, in turn, direct operations via bronze command. In the early days of the pandemic, Gold Command decided that they needed a Scientific Advisory Group to ensure that they based their decisions on the best possible evidence. Gold Command asked Professor John Wright (Head of the Born in Bradford project) to form this group, and he invited Professor Mark Mon-Williams to join this team (which included representatives from the Regional Universities as well as health services and the local authority). The COVID-19 Scientific Advisory Group (CSAG) met three times a week throughout the lockdown and oversaw a large number of projects - many of which included academics from our school.

Adult mental health and well-being

As well as acting as Deputy Chair of the British Psychological Society's COVID-19 Co-ordinating Group, Professor Daryl O'Connor has been involved in numerous research projects helping to understand the impact of COVID-19 on mental health, wellbeing and health protection behaviours. In April 2020, Daryl formed and led an expert group of psychologists nationally and internationally in order to establish the shorter and longer‐term priorities for research in psychological science. This expert group, also informed by a survey of 539 psychological scientists, published a position paper in which they identified seven key areas of research to support societies' recovery from COVID-19. These were: mental health, behaviour change and adherence, work, education, children and families, physical health and the brain, and social cohesion and connectedness.

Daryl has also been involved in the UK COVID-19 Mental & Wellbeing Study - a multi-wave national study of 3,077 adults in the UK. The initial findings of this study found that the first six weeks of the pandemic and lockdown had a major impact on the UK population's mental health and wellbeing. Suicidal thoughts increased over the first six weeks of the UK's lockdown (one in ten or 9.8% by the end of this period), however other factors related to suicide risk such as symptoms of anxiety, levels of defeat and entrapment decreased across the same period. Further analysis into sub-groups highlighting worse mental health outcomes during the pandemic for females, young people (aged 18-29), those from more socially disadvantaged backgrounds and those with pre-existing mental health problems. Males reported lower levels of depressive symptoms than females.

Alongside this research, Daryl, working with Arianna Prudenzi and Olivia Rogerson, are investigating vulnerability and resilience factors for mental health and wellbeing throughout the first and second wave of the pandemic. He is also working with Tmam Alghunaim and Dr Judith Johnson, investigating the impact of the pandemic on stress and burnout in UK surgeons.

Children's education and well-being

One of our major tasks was to obtain a better understanding of how the pandemic and lockdown was affecting children, young people and their families, with Dr Amanda Waterman and Dr Liam Hill playing a major role in this work.

One way we tackled this task was to administer a survey to the Born in Bradford cohort and explore the impact of the COVID-19 response on child wellbeing. This work was of National significance as our research team had baseline data that could provide confidence that we were measuring the impact of COVID-19 against a reliable baseline. Local and central government then used these data to inform their responses - with our scientists presenting evidence to a large number of government committees set up by the Department for Education and the Department of Health and Social Care.

Dr Amanda Waterman was awarded an ESRC Rapid Action grant that enabled the researchers to collect data from across 42 schools in Bradford about the impacts of COVID-19 on children's education and wellbeing, particularly focusing on vulnerable children and children with special educational needs and disability (SEND). The researchers were asked to write a report for the Department for Education which highlights the key issues raised by schools, and presents recommendations about how to address these issues. One of the major problems faced by schools is the difficulty in continuing to support vulnerable and SEND children, partly due to difficulties in delivering school-based interventions in a COVID safe way, but also the effect COVID is having on access to external services such as Speech and Language Therapy. Recommendations include finding ways to deliver services online, so that children do not have extended periods of time with no access to these essential support mechanisms. The report (authored by Dr Amanda Waterman and Dr Katy Shire in CAER) has also highlighted the anxiety and logistical difficulties for schools given the sometimes confusing, or last-minute guidance provided by local and central government.

Another issue was the significant effect on the mental health of pupils, their families, and teaching and support staff in schools. Schools have often gone well above and beyond their educational remit by providing emotional and practical support to families, especially in disadvantaged communities. In response to this, Amanda has highlighted the work being done by our partners in the Mental Health Trailblazers in Bradford, who have produced an overview of the resources available to support pupil, parents, and staff wellbeing. In addition, the Centre for Applied Education Research is running a series of webinars that bring together experts in public health, paediatrics and mental wellbeing with school leaders, allowing schools to gain the information they need to operate safely and with confidence. These initiatives will inform policy for implementing best-practice solutions at a national level.

Children's learning during the pandemic

Dr Hannah Nash is currently leading a study ( ICKLE) that examines the impact of COVID-19 class closures and remote learning on primary school children. It is the first detailed study of how the numeracy, literacy and language development of four and five year-olds has been affected by the ongoing pandemic. The study aims to give schools a better understanding of which children need extra support and in what areas of learning. In the longer term, study findings will contribute to national strategies aimed at reducing the negative impact of COVID-19 and provide key information in the event of any future school closures or lockdowns.

School Webinars

Our academics have supported social and emotional wellbeing in schools. We have organised webinars that connect our school leaders with medical experts in physical and mental health. The webinars shone a light on the immense pressures experienced by senior leaders within the school system. In response, we have created a 'School Staff Wellbeing Support Service' that provides peer-to-peer support, a listening line, and counselling support to headteachers throughout the Bradford District. In the week before Christmas, the Department for Education have arranged for us to speak with the leaders of 12 Districts to see whether other areas of the UK can adopt this model.

Increased risk to vulnerable children

Another aspect of the lockdown period was the number of vulnerable children placed at increased risk. The evidence suggests that the pandemic has placed even greater numbers of children at risk of vulnerability, with data emerging around sexual and criminal exploitation as well as the risks associated with poverty. The pandemic showed that many of our services are not well integrated and this means that many vulnerable children are 'under the radar' until they hit crisis point - at which point it becomes very difficult to deal with the problems they are experiencing. The pandemic also shone a spotlight on the lack of joined up working across services (e.g. health, education, social care), and the difficulties in creating policies that are tailored to the contextual issues associated with race, place and disadvantage. Central government is determined to address these problems and has formed a cross-Whitehall committee with representatives from all of the government departments ( Department for Education, Department of Health and Social Care, HM Treasury, Ministry of Justice etc). Psychology's Professor Mark Mon-Williams is part of this 'Digital Improvement across Government' group. The group have asked Mark to lead a national task force with the goal of creating an 'electronic vulnerability index' (eVI). The eVI will allow Local Authorities to connect data so that they can provide integrated and tailored services to children and young people.

Issues facing children and young people

The research we conducted during the pandemic showed that there are four major issues facing children and young people. These are: (i) Mental Health problems; (ii) Food Insecurity; (iii) Classroom Inequalities related to SEND; (iv) a Digital Divide where many of our disadvantaged children are unable to access online teaching materials. In order to tackle these issues, we created a partnership with Stanford University in California. We held a number of webinars with Stanford (and external partners and stakeholders) and identified specific projects that can help tackle these challenges. Professor Jason Halford is leading our response to food insecurity, Dr Amanda Waterman is the lead for classroom inequalities and Dr Faisal Mushtaq is leading work aimed at closing the digital divide.

Obesity

Professor Jason Halford has been working with colleagues at the European Association for the Study of Obesity (EASO), on the impact of COVID-19 with people living with obesity, and those who deliver clinical care through advocacy, education, guidance and training, as well as research.

To better understand the psychological impact of the pandemic on people living with obesity (PLWO) across Europe, they are conducting a study across 10 European countries (Denmark, England, France, Israel, Italy, Germany, Greece, Spain, Sweden, Portugal - representing differing experiences of and responses to COVID-19). The study also explores the impact of COVID-19 on the family and home environments of people living with obesity and investigates the impact of food marketing exposure and media messaging on health and eating behaviours. The research aims to identify protective strategies for obesity management and design interventions to support people during times of disruption. Evidence-based support for national and local health systems is desperately needed in developing useful tools for the treatment and long-term management of obesity during and beyond this pandemic.

Jason presented his research into COVID-19 and obesity at the British Nutrition Foundation meeting on the 24th November 2020.

Dr Stuart Flint has also undertaken and published research relating to the impact of COVID-19 on obesity and its impact. Working in collaboration with colleagues at Scaled Insights, based in Nexus, a number of outcomes have been published. This includes major contribution to Government publications on weight management; the work being published in Frontiers in Psychology and the Yorkshire Post; presentations at HETT - the UK's leading healthtech and digital health event; and numerous talks during the Leeds Digital Festival.

The work of CSAG and the involvement of our academics continues, and even involves some of our undergraduate student population. Born in Bradford take on a number of interns each year who spend a year working with the team under the supervision of Dr Katy Shire (who graduated from psychology after completing her BSc and PhD in the school). Three of these interns are in the process of completing a systematic review of 10,000 articles that report the outcomes of interventions following disasters around the world. A Research Fellow (Dr Nick Preston) in psychology is coordinating this work - work that will help government base its recovery plans on the best scientific evidence available.

We have played a major role in supporting local and central government throughout the pandemic - and our work will continue as we seek to help adults, young people and children recover from the consequences of the pandemic - a process that is likely to take many years.

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Our academic staff have played an integral role in researching what impact COVID-19 has had on society, to help the recovery process and see what lessons can be learned from the pandemic.

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A duty of care and a duty to teach: educational priorities in response to Covid 19.

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A duty of care and a duty to teach: educational priorities in response to Covid 19.

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This study explores the challenges the COVID-19 crisis sets primary school teachers.

These challenges will be considered in the light of the diverse roles primary schools find themselves playing in their local communities, and in recognition that roles will vary depending upon levels of social disadvantage.

This research has been funded by UKRI in partnership with the ESRC as part of their call to address the health, social, economic and environmental impacts of the COVID-19 outbreak.

The project runs for four months, from 19 May to 8 September 2020.

Background

This is a rapid turnaround project designed to inform education policy decision-making in the light of the Covid crisis. The project has documented teachers' immediate responses to the COVID-19 crisis from the start of lockdown onwards, including their reflections on how education might need to change.

It has compared the dilemmas teachers faced with the concerns expressed in public debate on the impacts of COVID-19 on education, and used a Rapid Evidence Assessment to identify relevant research evidence that can be brought to bear on the likely impacts of school shutdowns.

The findings will be used to:

Inform decisions about the terms upon which schools re-open, including any implications for high stakes tests and inspection in the coming year.

Set an agenda for public reflection on the role that primary schools can play in building a more resilient education system in partnership with their local communities.

The main aims of the project are:

to explore the challenges that the COVID-19 crisis sets primary schools as teachers weigh a duty of care (for their pupils' well-being and welfare) and a duty to teach (given their responsibilities for curriculum delivery) in their interactions with families during the lockdown

to explore whether and how actions taken during the crisis are leading primary schools and their staff to a) reconsider how they can best support their communities, particularly those living in disadvantage, b) and/or rethink what is most important in teaching and learning, with a focus on the literacy curriculum

to inform decisions that will need to be made about how schools resume 'business as usual' post lockdown, including any implications for high stakes tests and inspection

to consider the legacy from the crisis in terms of a more general public conversation about shared purposes and values in education going forward, once schools fully reopen.

Methodology

The project uses the following methods:

Teacher surveys - of their current actions and future priorities in the light of COVID-19

Documentary collection - sampling from the online conversations on what schools should be doing during the Covid crisis, to which different stakeholder groups are contributing (the research community, professional bodies, politicians, parent groups and the media)

A Rapid Evidence Assessment - of the peer-reviewed research literature that can contribute to understanding the impact of school shutdowns in the areas of most concern in the public conversation. With a focus on outcomes relevant to building school-community resilience, and assessment of the different strategies adopted to mitigate risks.

Principal Investigator

Co-Investigators

Expert advisor

Blogs and articles

Reports and publications

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A duty of care and a duty to teach: educational priorities in response to the COVID-19 crisis

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This study explores the challenges the COVID-19 crisis sets primary school teachers. These challenges will be considered in the light of the diverse roles primary schools find themselves playing in their local communities, and in recognition that roles will vary depending upon levels of social disadvantage.

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The impact of COVID-19 on education today.

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The impact of COVID-19 on education today.

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When pupils need to self-isolate, the DfE expects schools to be able to immediately offer them access to remote education; this ensure that no students are at a disadvantage and do not fall behind with their lessons.

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only 51% of households earning between £6000-10,000 had home internet access compared with 99% of households with an income of over £40,001.

https://www.cam.ac.uk/stories/digitaldivide

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The impact of Covid 19 on children and families.

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The impact of Covid 19 on children and families.

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By Danielle Hamilton BScN, RN

COVID-19 has rattled our world, with devastating impacts on children and families across the globe. From job loss to supply shortages, parents and children have been faced with new and evolving sources of stress. Parents are concerned about not only the physical health of children and protecting them from the virus, but their emotional and mental health as well. Children were quickly removed from their schools and daycares to reduce their risk and transmission, and later parents were faced with the difficult decision to have them return or not. Childcare support became unstable, and the previous routines and structure were no longer attainable. As parents engage in the balancing act of working from home (if you were fortunate enough to have the option) while ensuring children were participating in their education, they also worry about providing children with stimulation and activity while maintaining safety.

International data indicate that the numbers of children who are symptomatic, test positive for, or die from the virus is very small compared to older age groups (Our World in Data, 2020). Since the numbers of children affected are relatively low, they may appear to be a small or insignificant, but if we were to ask the parents of these young adults and children, would they agree? Many children with COVID experience mild cases, which are still a cause for worry for parents and families as they track potential older, vulnerable people who have been in contact with their children Some children experience more severe symptoms. Foster et al. (2020) conducted research involving 57 pediatric patients with COVID-19 during the initial phase of the outbreak. Of these patients, 83% presented with a fever or cough, and all who required admission to hospital had secondary diagnoses including diabetic ketoacidosis, vaso-occlusive crisis, acute chest syndrome, asthma exacerbation, altered mental status from hypernatremia, reactive arthritis, or appendicitis (Foster et al., 2020).

It is not to say that children who have not been officially diagnosed with the virus have not been affected. Children are vulnerable and at an increased risk of abuse during school closures (Cluver et al., 2020). COVID-19 has also presented some challenges related to food security for some children (Pérez‐Escamilla, Cunningham & Moran, 2020). They are at risk of increased stress and reduction of supportive resources secondary to this pandemic which may impact their mental health (Clemens., et al., 2020). Children are also susceptible to related posttraumatic distress (Zhou, 2020). It has also been observed that there is a reluctance to seeking medical attention due to fear of contracting COVID-19 which has resulted in decreased admissions but increased severity when care is obtained (Ciacchini et al., 2020).

How do we as health care professionals support children and families through COVID-19? It has been recommended that psychological services that include social, school, and family systems be provided (Zhou, 2020). By providing direct or indirect services and resources that support children, parents, and schools/teachers we can encourage sustainable recovery (Zhou, 2020). Although the pandemic has resulted in limited access to services in many areas, we also need to be innovative with delivery and improve access. We have already seen drastic changes in the delivery of care through virtual means and deployment of resources as required (Price & Campbell, 2020). We must continue to adapt and support policy and program development and change to best suit the needs of children and families.
References
Our World in Data. (2020). Mortality Risk of COVID 19. https://ourworldindata.org/mortality-risk-covid#case-fatality-rate-of-covid-19-by-age

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Impact of COVID-19 on Children and Families | Evidence-Based Nursing blog

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By Danielle Hamilton BScN, RN COVID-19 has rattled our world, with devastating impacts on children and families across the globe. From job loss to supply shortages, parents and children have been faced with new and evolving sources of stress. Parents are concerned about not only the physical health of children and protecting them from the [...]Read More...

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The level of challenge and nature of remote provision will vary across school; depending on the children’s ages and needs. It is essential that schools provide flexibility to remote teaching to ensure that the education provided for children suits their circumstances. 

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For younger children in particular, remote education will require more involvement and support from parents and carers. 

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at least 700,000 children have missed out on schoolwork due to a lack of computer or internet access, and cannot complete their homework.

https://www.bbc.co.uk/newsround/53047270

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The DfE is closely working with schools to provide and develop evolving guidance based on remote teaching and the current experiences and practice of teachers.

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An estimated 9% of families in the UK do not have a laptop, desktop or tablet at home.

https://www.childrenscommissioner.gov.uk/2020/08/18/children-without-internet-access-during-lockdown/

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https://www.edge.co.uk/sites/default/files/documents/covid-19_report_final_-_web.pdf