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Gamified Learning Platforms & Behaviorism 

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Gamified Learning Platforms & Behaviorism 

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Utilizing gamified learning platforms where students earn points or badges for completing tasks or answering questions correctly reflects behaviorist principles by incentivizing desired behaviors. Through game elements such as rewards, levels, and leaderboards, learners are motivated to engage with the material and achieve learning objectives.

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Top 10 gamified learning platforms: Find the best fit for you

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List of gamified learning platforms used commonly. The blog will focus on the gamification options provided by widely used tools in training.

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Data Visualization Projects & Cognitivism

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Data Visualization Projects & Cognitivism

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Data visualization projects help students interpret and present complex information in a visually engaging way. These activities enhance comprehension and retention of abstract concepts, supporting Cognitivism by facilitating the processing and understanding of large amounts of data.

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Gamification incorporates game design elements in non-game contexts to motivate and engage learners. This approach aligns with Behaviorism by using rewards and feedback to reinforce desired behaviors, making it effective in both virtual and face-to-face learning environments.

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Virtual Reality (VR) offers immersive learning experiences that enhance cognitive processes, such as attention and memory. By simulating realistic scenarios, VR helps in encoding information into long-term memory, making it a powerful tool in supporting Cognitivism.

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Problem-Based Learning engages students in cognitivist learning by presenting authentic, complex problems that require critical thinking and problem-solving skills, fostering deeper understanding and knowledge construction.

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Cognitive engagement in the problem-based learning classroom

Abstract

The objective of the present study was to examine to what extent autonomy in problem-based learning (PBL) results in cognitive engagement with the topic at hand. To that end, a short self-report instrument was devised and validated. Moreover, it was examined how cognitive engagement develops as a function of the learning process and the extent to which cognitive engagement determines subsequent levels of cognitive engagement during a one-day PBL event. Data were analyzed by means of confirmatory factor analysis, repeated measures ANOVA, and path analysis. The results showed that the new measure of situational cognitive engagement is valid and reliable. Furthermore, the results revealed that students' cognitive engagement significantly increased as a function of the learning event. Implications of these findings for PBL are discussed.

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Introduction

Cognitive engagement in the classroom can be characterized as a psychological state in which students put in a lot of effort to truly understand a topic and in which students persist studying over a long period of time. The present study is about this kind of cognitive engagement and how it emerges in the problem-based learning (PBL) classroom.

PBL is an approach to higher education that has the following characteristics. Small groups of students discuss a problem guided by a tutor. Based on the discussion about the problem, students generate learning goals for subsequent self-directed learning. As such, students have a choice in deciding which learning goals they would pursue in order to adequately deal with the problem. After a period of self-directed learning, students share what they have learned about the topic and test whether their new understanding of the problem is now more accurate and elaborate than before. Once students are satisfied with their learning outcomes, they engage with a new problem and the cycle starts all over again (Hmelo-Silver 2004; Schmidt 1993).

PBL can be interpreted as a form of cognitive-constructivist learning, based on at least three assumptions (Schmidt et al. 2009). The first assumption is that in PBL students engage in theory construction. With the help of their peers they develop an initial theory about the phenomena described in the problem. Subsequently, self-directed learning activities (e.g. reading books or consulting internet resources) serve to test the initial theory against the literature thereby elaborating and changing and deepening their understanding of the topic. The second assumption is that the use of authentic problems or real-life problems encourage students to become interested in the topic at hand and as a consequence helps them gaining a deeper understanding of the principles or processes underlying the problem. The third assumption is that being in the position to identify one's own learning goals in collaboration with peers fosters a feeling of autonomy, agency, and empowerment. Being autonomous from the direct intervention of a teacher and feeling in charge of one's own learning is supposed to result in increased cognitive engagement with the topic to be learned, which eventually encourages deeper understanding of it.

There is some empirical evidence suggesting that what students do in the tutorial group is indeed attempting to construct a mental model or "theory" that explains the phenomena described in the problem. For instance, De Grave et al. ( 2001) showed students videos of their own contributions to a tutorial discussion and asked them to recall what they were thinking. This stimulated recall procedure in combination with verbatim transcripts of the verbal interaction in the group suggested that indeed theory building, and to a lesser extent, data exploration and hypothesis evaluation were central to the thoughts and verbal utterances of the students.

Support for the second assumption, i.e. that the authentic character of the problems results in higher levels of student interest, can be found in a study by Rotgans et al. ( 2010). In their study they examined whether there are differences in students' interest between a problem-based learning and a conventional, direct-instruction, primary school mathematics classroom. The results showed that the problem-based group, which worked on an authentic problem, was significantly more interested in a particular subject than the direct-instruction group that worked on more abstract mathematical examples and definitions of the same subject.

The third assumption, namely that autonomy in learning leads to more extensive cognitive engagement, has not been studied to the same extent as the previous ones. Following suggestions in the self-determination literature, Deci ( 1992) has proposed that classrooms that promote student autonomy and choice increase student's engagement with the task at hand (see also Cordova and Lepper 1996). Deci et al. ( 1991) pointed out that choice has a positive effect on interest and engagement because people have an innate psychological need for competence, belonging, and autonomy. In self-determination research, having a choice is a means to satisfy that need for autonomy. In the PBL classroom students have the choice to determine what they wish to study (i.e. select their own learning goals and conduct their individual self-study), which, according to self-determination theory, should lead to a feeling of autonomy. Feeling autonomous and empowered in the classroom is expected to have a motivating effect encouraging students to engage themselves cognitively with the task at hand. Following this line of thought, we hypothesized that when students feel autonomous (from the tutor and the team members) they would display more cognitive engagement with the task. We expected that this would most likely happen when students are in charge of their own learning during individual self-study.

Cognitive engagement is defined as the extent to which students' are willing and able to take on the learning task at hand. This includes the amount of effort students are willing to invest in working on the task (Corno and Mandinach 1983), and how long they persist (Richardson and Newby 2006; Walker et al. 2006). Cognitive engagement has traditionally been operationalized by measuring the extent of students' homework completion, class attendance, extra-curricular participation in activities, or their general interactions with the teachers, and how motivated they seem while engaging in classroom discussions (Appleton et al. 2006). This description of cognitive engagement suggests that it is considered by most authors a more or less stable trait of students, independent of the context. We suggest that cognitive engagement is more or less dependent on the task at hand because the task determines the extent of students' autonomy. For instance, working with groups and engaging in discussions, searching for information on the internet, or listening to a lecture is likely to result in different levels of cognitive engagement because of different levels of autonomy. Listening to a lecture is arguably the least cognitively engaging since under such circumstances there is little to no student autonomy. On the other hand, when students independently search for information on the internet-that is, when students engage in self-initiated information-seeking behaviors-the level of autonomy should be relatively high and thus lead to more cognitive engagement. Working in groups and engaging in discussions could result in either high or low feelings of autonomy, depending on the group dynamics. For example, if there are domineering peers in the group, a student may feel less autonomous and engages less cognitively as opposed to a group that works well together. In short, we suggest that the level of autonomy is inherently related to an activity or task and largely determines the degree to which students engage cognitively with that activity or task.

As a consequence, if the task parameters change during a learning event-as it is the case in PBL-one would expect that students perceive different levels of autonomy and consequently engage differently. For instance, in PBL during the initial phase of defining the problem, students have to work in teams under the guidance of a tutor. During this phase one could expect that students' autonomy would be relatively low. However after this, students undertake independent self-study, during which one could expect that their autonomy would be relatively higher and thus they would be more cognitively engaged. After this, students converge and share their findings, which would result again in a decrease of autonomy and cognitive engagement.

To test whether this is indeed the case, one has to be able to measure changes in cognitive engagement over a learning event. Existing instruments are designed to measure generally more stable, trail-like cognitive engagement, which means that the grain size of these instruments is too large to pick up fairly small contextual variations. For instance, Appleton and colleagues developed the Student Engagement Instrument or SEI, (Appleton et al. 2006) to measure students' cognitive engagement (perceived relevance of school) and psychological engagement (perceived connection with others at school). Although the SEI goes beyond the conventionally used broad indictors of engagement, such as homework completion, attendance, and extracurricular participation, the instrument's grain size is still too large to adequately measure contextual changes in engagement over a learning event. This becomes apparent when examining the items of the SEI, which are rather broad and related to school engagement in general (e.g. "I enjoy talking to the teachers here", "Most of what is important to know you learn in school", "Learning is fun because I get better at something", "What I'm learning in my class will be important in my future", and "I feel like I have a say about what happens to me at school").

The same applies to a study conducted by Ahlfeldt et al. ( 2005) in which they used various elements of the National Survey of Student Engagement (Carini et al. 2006; Kuh 2001) to measure student engagement in the classroom. Although the authors only selected items from the National Survey of Student Engagement that in their view measured student engagement at the classroom level with relation to collaborative learning, cognitive development, and personal skills development, the grain-size of the items is again too large to pick up context-dependent variations during the lesson itself (e.g. "I worked with classmates outside of class to complete class assignments", "To what extent has this course emphasized the mental activities listed below?" "Memorizing facts, ideas or methods from your course and readings so you can repeat them in almost the same form").

Due to the absence of more specific cognitive engagement measures, researchers also resorted to using scales and sub-scales from existing instruments, which to some degree resemble cognitive engagement. In numerous studies, researchers adapted self-regulated learning strategy scales (e.g. DeBacker and Crowson 2006; Dupeyrat and Mariné 2005; Meece et al. 1988; Metallidou and Vlachou 2007). For instance, Metallidou and Vlachou ( 2007) used various self-regulatory learning sub-scales from the Motivated Strategies of Learning Questionnaire (Pintrich et al. 1991) such as rehearsal, elaboration, and organizational strategies, as a measure of students' cognitive engagement. Although we agree that learning strategies manifest themselves in different forms of cognitive engagement, we have reservations whether it is admissible to simply rename self-regulated learning constructs and use them as measures of cognitive engagement.

Considering that there seem to be no suitable instruments available to fit the objectives of the present study to measure cognitive engagement as it happens in the classroom (in real time), we saw the need to construct and validate a short self-report questionnaire to determine students' " situational cognitive engagement". To differentiate this new measure from more general traditional measures of cognitive engagement, as discussed above, we added the designation "situational" to stress the contextual dependence of this measure. The new situational cognitive engagement measure is composed of three overlapping facets: (1) how students perceive their present engagement with the task, (2) how they rate their effort and persistence while working on the task, and (3) how much they feel absorbed by the learning task, for instance, whether it makes them forget everything around them. It is important to note that all three facets measure ongoing cognitive engagement and try to capture the activity of being engaged. This is conceptually different from existing measures of cognitive engagement, which are typically administrated at the end of the course or a semester and require students to make summative judgments of how engaged they generally were during a particular course spanning several weeks. To capture the dynamic aspect of engagement during class, we generated items that measure students' effort (Blumenfeld et al. 2006; Corno and Mandinach 1983; Volet 1997; Wolters 1999) and how willing they are to persist on the task at hand (Ainley et al. 2002; Pintrich and De Groot 1990; Prenzel 1992; Richardson and Newby 2006; Walker et al. 2006). As an ultimate form of being engaged in learning we added an item measuring flow, that is, being fully emerged in learning and forgetting everything around oneself (Csikszentmihalyi 1975; Csikszentmihalyi and Csikszentmihalyi 1988).

The first objective of the present study was to establish the construct validity of this new measure (Study 1). To test whether students' cognitive engagement changes during PBL, we administered the situational cognitive engagement measure five times during a 1-day PBL event at a polytechnic in Singapore (Study 2). Eleven applied-science classes participated in the study. Students worked on one problem during the course of 1 day. At the polytechnic the PBL day is divided into five distinct learning phases: (1) the problem definition phase (i.e. initial theory construction and identification of learning goals); (2) Initial self-study (i.e. students do a preliminary search, or self-study, to verify their hypothesized theory to explain the problem and to verify the adequacy of identified learning goals); (3) Initial findings sharing phase (i.e. student share the insights gained during the preliminary self-study and the tutor may contribute by asking questions to help students further structure their learning goals); (4) Self-study phase (i.e. student engage in further individual self-study to look for answers to the identified learning goals); and (5) Presentation and elaboration phase (i.e. student share their insights gained during self-study, synthesize their findings, and evaluate whether they have addressed all learning goals adequately). After each phase we administered the situational cognitive engagement measure. We expected the following pattern to emerge: During the phases in which students come together as a team, their cognitive engagement would be fairly low because contributions of peers and their tutor may constrain the level of autonomy experienced. On the other hand, if students do individual self-study, their autonomy would be higher and so does their level of cognitive engagement. Therefore, we expected to observe a wave-like pattern for cognitive engagement during the day; depending on the particular phase of the learning process engagement would start at a fairly low level, subsequently increase and then decrease, followed by another wave of increase and decrease.

Study 1

The objective of the first study was to establish the reliability and construct validity of a short measure of situational cognitive engagement. To that end, we administered a four item self-report measure after students had completed the first phase of the PBL-cycle: the problem-definition phase. The construct validity was established by means of confirmatory factor analysis of two samples: (1) an exploration sample, and (2) a cross-validation sample. This approach is in line with common practices in SEM (Byrne 2001). We computed the reliability of the scale by calculating the coefficient H (Hancock and Mueller 2001).

Method

Participants

Two samples were used in the validation study: (1) a smaller exploration sample ( N = 61) for instrument construction and (2) a larger confirmation sample ( N = 312) for cross-validation of the instrument. The average age of the participants for the exploration sample (62% female) was 20 years (SD = .98). The average age of the participants for the cross-validation sample (52% female) was 20 years (SD = 1.45). All students were enrolled in science-related modules at a polytechnic in Singapore.

Educational context

In this polytechnic, the instructional method is problem-based learning (PBL) for all its modules and programs. In this approach five students work together in one team under the guidance of a tutor. Each class comprises four to five teams. Unique to this polytechnic's approach to PBL is that students work on one problem during the course of each day (Alwis and O'Grady 2002). This means that students deal with one problem each day in all modules. A typical day starts with the presentation of a problem. Students discuss in their teams what they know, do not know, and what they need to find out. By doing so, students activate their prior knowledge, come up with tentative explanations for the problem, and formulate their own learning goals. Subsequently, a period of self-study follows in which students individually and collaboratively try to find information to address the learning goals (Hmelo-Silver 2004; Schmidt 1983, 1993; Schmidt et al. 2009). At the end of the day the five teams come together to present, elaborate, and synthesize their findings.

Measure

A measure of students' situational cognitive engagement was devised which consisted of three elements, measured by four items: (1) engagement with the task at hand (item: " I was engaged with the topic at hand"), (2) effort and persistence (item: " I put in a lot of effort"; "I wish we could still continue with the work for a while"), and (3) experience of flow or having been totally absorbed by the activity (item: " I was so involved that I forgot everything around me") (Csikszentmihalyi 1975; Krapp and Lewalter 2001; Prenzel 1992; Schraw et al. 2001). The items were scored on a 5-point Likert scale: 1 ( not true et al for me), 2 ( not true for me), 3 ( neutral), 4 ( true for me), and 5 ( very true for me).

Procedure

The situational cognitive engagement measure was administered after the problem-definition phase, that is, after students had engaged in theory construction and after they had generated learning goals. The problem-definition phase took about 20 min. Responding to the questionnaire took about 20 s. After we had completed the validation with the exploration data, we repeated the validation procedure for a different sample, the cross-validation sample. Cross-validation is a necessary step in establishing the construct validity of new measure because it allows the researcher to test how stable the measure is in different contexts and samples.

Analysis

The construct validity of the situational cognitive engagement measure was established by means of confirmatory factor analysis (Byrne 2001). With the present study we did not intend to study consequential or predictive validity. The assumption was that all four items were manifestations of one underlying factor. Parameter estimates were generated using maximum likelihood and tests of goodness of fit. Chi-square accompanied by degrees of freedom, sample size, p-value and the root mean square error of approximation (RMSEA) were used as indices of absolute fit between the models and the data. The Chi-square is a statistical measure to test the closeness of fit between the observed and predicted covariance matrix. A small Chi-square value, relative to the degrees of freedom, indicates a good fit (Byrne 2001). A Chi-square/ df ratio of less than 3 is considered to be indicative of a good fit. RMSEA is sensitive to model specification and is minimally influenced by sample size and not overly affected by estimation method (Fan et al. 1999). The lower the RMSEA value, the better the fit. A commonly reported cut-off value is .06 (Hu and Bentler 1999). In addition to these absolute fit indices, the comparative fit index (CFI) was calculated. The CFI value ranges from zero to one and a value greater than .95 is conventionally considered a good model fit (Bentler 1990; Byrne 2001).The reliability of the measure was determined by calculating Hancock's coefficient H. The coefficient H is a construct reliability measure for latent variable systems that represents a relevant alternative to the conventional Cronbach's alpha. According to Hancock and Mueller ( 2001) the usefulness of Cronbach's alpha and related reliability measures is limited to assessing composite scales formed from a construct's indicators, rather than assessing the reliability of the latent construct itself as reflected by its indicators. The coefficient H is the squared correlation between a latent construct and the optimum linear composite formed by its indicators. Unlike other reliability measures the coefficient H is never less than the best indicator's reliability. In other words, a factor inferred from multiple indicator variables should never be less reliable than the best single indicator alone. Hancock recommended a cut-off value for the coefficient H of .70.

Results and discussion

First, the exploration sample was analyzed. The model fit statistics showed that the data fitted the hypothesized model very well. The Chi-square/ df ratio was .17, p = .84, CFI = 1.00 and RMSEA = .00. All factor loadings were statistically significant and ranged from .51 to .96, with an average of .70. See Table 1 for an overview of the results.

Table 1 Model fit statistics of the situational cognitive engagement measure for the exploration and cross-validation samples

We then cross-validated the model with a larger sample. The results revealed that the data fitted the model equally well. The Chi-square/ df ratio was .02, p = .94, CFI = 1.00 and RMSEA = .00. All factor loadings were statistically significant and ranged from .38 to .85, with an average of .58.

The reliability for both samples was determined by calculating Hancock's coefficient H. The coefficient H for the exploration sample was .93 and for the cross-validation sample .78. Overall, the results demonstrate that for both independent samples the psychometric characteristics of the situational cognitive engagement measure are adequate.

Study 2

The first objective of Study 2 was to examine the degree to which students are cognitively engaged during PBL across the five PBL phases: (1) the problem definition phase; (2) Initial self-study; (3) Initial findings sharing phase; (4) Self-study phase; and (5) Presentation and elaboration phase. We expected that cognitive engagement during the first phase would be relatively low; then it would increase during the first self-study phase; decrease again during group discussion; increase during the longer self-study phase; and eventually decrease again during the presentation and elaboration phase. This hypothesis was based on the assumption that when students come together in the team and with the tutor, their autonomy would be relatively lower because of the constraints on choice provided by group and tutor. Under this hypothesis, during self-directed learning their level of cognitive engagement would be relatively higher.

The second objective of this study was to examine the extent to which situational cognitive engagement in one phase determines a students' cognitive engagement during a next phase. We hoped to find answers to the questions: does it matter how cognitively engaged student are during the problem definition phase in predicting their subsequent engagement during self-study? Or, does the initial self-study phase predict students' cognitive engagement during the second, more elaborate self-study phase?

Method

Participants

The sample consisted of 208 participants (51% female) from an applied science module at the same polytechnic as in Study 1. The participants' average age was 20 years (SD = 1.45).

Measures

Situational cognitive engagement

The measure for students' situational cognitive engagement validated in Study 1 was administered for this study. All items were scored on a 5-point Likert scale: 1 ( not true at all), 2 ( not true for me), 3 ( neutral), 4 ( true for me), and 5 ( very true for me). The coefficient H for all five cognitive engagement measurements ranged from .70 to .79 (average = .77).

Academic achievement

Academic achievement was determined by means of students' course grades. The course grades were based on the results of written achievement tests and class performance.

Procedure

The situational cognitive engagement measure was administered during the five phases in the 1-day PBL process (see Fig. 1 for an overview of the administration during the day).

Measurement occasions of the situational cognitive engagement measure during a 1-day problem-based learning process

The first administration took place after the problem-definition phase in which they generated learning goals. The second administration was after the initial self-study phase. In this phase students did an initial search in the learning resources or the internet to verify whether their initial theories about the problem were correct. The students then converged and had a quick discussion with the group to share their initial findings. A tutor was present during this session and engaged in questioning the students about their findings and whether their learning goals adequately address the problem. Subsequently, the situational cognitive engagement measure was administered for the third time. Student then went out for 2-h self-study after which the fourth situational cognitive engagement measure was administered. Students shared their findings and engaged in elaboration about the problem and whether they have adequately addressed all learning goals. After this, the engagement measure was administered for the fifth and last time.

Analysis

As a first step in the analysis, we generated zero-order correlations to inspect how the five repeated measurements of situational cognitive engagement are related. Moreover, we calculated mean values of all five measurements. Potential mean level differences between the measurements were determined by means of a one-way repeated measures ANOVA with LSD comparisons of the means. Subsequently, the relationships between the five situational cognitive engagement measurement occasions were analyzed using path analysis. In the path analysis, we tested a sequential causal model (see Fig. 1). This entails that one measurement leads to the next: situational cognitive engagement measure 1 leads to situational cognitive engagement measure 2, situational cognitive engagement measure 2 leads to 3, and so on. To allow for the possibility that the relationships are not entirely sequential, e.g. early engagement may lead to engagement later on during the day, we first tested an explanatory model which allowed for all possible relationships (see Fig. 1, dotted lines). In the final model reported in the Results and Discussion section we only retained the relationships, which were statistically significant; all non-significant relationships were removed. For the model, Chi-square accompanied with degrees of freedom, p-value, and the root mean square error of approximation (RMSEA) were used as indices of absolute fit between the models and the data.

Results and discussion

First, intercorrelations between the five measurement occasions were calculated (see Table 2). All correlation coefficients were statistically significant and were medium to strong, ranging from .15 to .88, suggesting that the level of cognitive engagement in one situation is associated with another during the one-day PBL process as well as students' academic achievement (i.e. course grades at the end of the semester).

Table 2 Intercorrelations between the situational cognitive engagement measurements and academic achievement during one-day problem-based learning

The repeated measures one-way ANOVA revealed that there were significant differences between the five situational cognitive engagement measurements in absolute sense: F (4,207) = 47.53, p < .01 (eta-squared = .19). The pairwise LSD comparisons revealed that the first two measurements were not significantly different (see Fig. 2: M(1) = 3.33 vs. M(2) = 3.37, p = .24).

Mean values of the situational cognitive engagement measurements (SCEM) during a 1-day problem-based learning event

This outcome suggests that there is not a significant difference in the cognitive engagement phases when students generate theories and learning goals and do an initial search in the resources to verify their initial theories to explain what is going on in the problem. However, after that, when the students come together and discuss what they have found during the initial self-study phase, their level of cognitive engagement increased significantly ( M(1) = 3.33 vs. M(3) = 3.59, p < .01 and M(2) = 3.37 vs. M(3) = 3.59, p < .01). The results suggest that students' level of cognitive engagement increases not so much during the initial self-study phase, as we had expected, but when they come together and share their findings with each other and the tutor. After this, students engaged in self-study leading to another significant increase in cognitive engagement ( M(3) = 3.59 vs. M(4) = 3.66, p = .01). After self-study students seemed to have reached a peak in cognitive engagement; during the presentation and elaboration phase their levels of cognitive engagement remained the same ( M(4) = 3.66 and M(5) = 3.66, p = .99). Overall, the data suggests that during the PBL day cognitive engagement increases gradually and does not develop in a wave-like pattern as we had hypothesized.

As a next step in the analysis we investigated how the five cognitive engagement measurements were related to each other. For instance, does one level of cognitive engagement influences the next or does an earlier measurement predict a measurement during a later phase of the learning process. Figure 3 depicts the model with the path coefficients indicating the strength of relation between the measurements. The model fitted the data well: Chi-square/ df ratio = 2.06, p = .10, CFI = 1.00, and RMSEA = .07.

Causal relationships between the five measurement occasions of situational cognitive engagement during a 1-day PBL event. Note: Numbers above the arrows represent standardized regression weights. All standardized regression weights are statistically significant at the 1% level

The results revealed that the cognitive engagement measurements are strongly related to each adjacent measure in time. That is, if a student is cognitively engaged during the problem definition phases he or she is likely to be engaged during the next phases as well. There were also some weaker non-sequential relationships, for instance, between the first measure and the second and the second and the fourth and fifth. Overall, 81% of the variance in the last situational cognitive engagement measure could be explained by the preceding ones.

General discussion

The objective of the present study was to examine the underlying assumption that students in PBL have a large degree of autonomy (i.e. when students engage in individual self-study), which is expected to result in cognitive engagement with the topic at hand. We hypothesized that when students experience a feeling of being autonomous from the tutor or the group, they would engage more cognitively with the problem (Deci 1992; Deci and Ryan 2004; Deci et al. 1991). To test the extent to which this is the case we first devised and validated a short self-report instrument to measure students' situational cognitive engagement in the classroom. Subsequently, we examined how situational cognitive engagement develops as a function of the learning process in PBL and the extent to which situational cognitive engagement during the learning process determines subsequent levels of cognitive engagement. Data were collected from applied-science courses at a polytechnic in Singapore.

The results of the construct validation and cross-validation study suggest that the four-item instrument is a reliable and valid measure to determine students' situational cognitive engagement in the classroom. As such, we used it for the subsequent analyses. Following Self-determination theory (Deci and Ryan 2004) under which autonomy is defined as the degree to which individuals feel volitional and responsible for their initiation of their behavior (Williams 2004), we hypothesized that students would have the highest feelings of autonomy (and thus would engage more) during self-study because during self-study they are expected to feel most volitional in their actions and are most responsible for their learning (Assor et al. 2002; Deci 1992; Flowerday and Schraw 2003; Ryan and Deci 2000). Following this point of view we expected that when students meet with the group and the tutor their feeling of autonomy would be reduced (relative to self-study), leading to less cognitive engagement with the problem. Because students first meet with the group, then break away for initial self-study, meet again with the group and the tutor, break away for self-study, and finally meet again with the group and the tutor, we expected to observe a wave-like pattern of cognitive engagement to emerge during the one-day PBL event.

The results of our analyses did however not support this hypothesis. Students' cognitive engagement did not progress in a wave-like pattern, but it increased significantly and consistently over the day. Strongest evidence against our hypothesis is that students' situational cognitive engagement increased significantly not during the first self-study phase, but when students met with the group thereafter to discuss their finings. Situational cognitive engagement increased significantly again during the second self-study phase. Our data suggest that students' situational cognitive engagement is not influenced by changes in task demand and associated feelings of autonomy, but situational cognitive engagement is more a function of the learning event itself: if students progress with their learning in PBL, their situational cognitive engagement increases.

Considering this outcome, we offer an alternative hypothesis. We propose that students' feelings of autonomy and situational cognitive engagement are a direct function of a students' knowledge construction. During the early stages of the problem day (i.e. during the problem-definition phase), students struggle to come up with adequate theories to explain the phenomena described in the problem. Struggling to explain the problem is expected (and intended) because students lack relevant knowledge as they are supposed to engage in theory construction. At this stage students largely depend on the elaborations with the other team members and the questioning of the tutor. As such, choice and autonomy are expected to be generally low. However, as students gain a deeper understanding of the topic, they gradually depend less on the support of their peers and the tutor, because they have gained more knowledge to direct their own learning. With increasing knowledge, the knowledge of possible (learning) choices also increases, which translates into a feeling of autonomy and consequently higher levels of situational cognitive engagement.

In conclusion, we propose that indeed, autonomy plays a significant role in students' situational cognitive engagement during PBL. However, unlike we hypothesized, students' autonomy seems less dependent on the phases during which they alternatively meet the group and engage in self-study, but on the knowledge students gain during their learning. The progression of knowledge and understanding of the topic seems to determine students' autonomy and thus their increased situational cognitive engagement. In simple terms: more knowledge, more autonomy, more self-determination, more situational cognitive engagement.

This finding opens up new areas of research for self-determination theory. Reeve ( 2004) has proposed that there is empirical evidence to support two conclusions about self-determination theory and its significance for education: (1) autonomously-motivated students thrive in educational settings; and (2) students benefit when teachers support their autonomy. Indeed, there is considerable evidence linking these two factors to positive educational outcome, such as higher academic achievement (Boggiano et al. 1993; Miserandino 1996), higher rates of retention (Vallerand and Blssonnette 1992; Vallerand et al. 1997), higher perceived competence (Ryan and Grolnick 1986), greater conceptual understanding (Boggiano et al. 1993; Flink et al. 1990), greater creativity (Amabile 1985; Koestner et al. 1984), and higher self-esteem (Deci et al. 1981; Ryan and Deci 2000). However, largely missing from the current research agenda is the consideration of the significant role knowledge may play in autonomy and autonomy-supportive behavior.

Needless to say, further research needs to be carried out to empirically test whether and how autonomy and knowledge (development) are interlinked. We suggest a fruitful approach would be to include, besides the situational cognitive engagement measure, a measure of students' autonomy and their factual knowledge in the investigation to examine how these three factors are related and how they develop during the stages of student learning in PBL.

References

Alwis, W. A. M., & O'Grady, G. (2002). One day- one problem at Republic Polytechnic. Paper presented at the 4th Asia-Pacific Conference on PBL.

Rotgans, J. I., Lai, K. C., Ong, H. L. C., Choo, H. K., & Schmidt, H. G. (2010). Situational interest in mathematics: A microanalytical comparison of problem-based learning vs. direct instruction. Instructional Science (Submitted).

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1. Republic Polytechnic, Centre for Educational Development, 9 Woodlands Avenue 9, Singapore, 738964, Singapore

Jerome I. Rotgans

2. Erasmus University, Rotterdam, The Netherlands

Henk G. Schmidt

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Correspondence to Jerome I. Rotgans.

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Rotgans, J.I., Schmidt, H.G. Cognitive engagement in the problem-based learning classroom. Adv in Health Sci Educ 16, 465-479 (2011). https://doi.org/10.1007/s10459-011-9272-9
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Keywords

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Cognitive engagement in the problem-based learning classroom - Advances in Health Sciences Education

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The objective of the present study was to examine to what extent autonomy in problem-based learning (PBL) results in cognitive engagement with the topic at hand. To that end, a short self-report instrument was devised and validated.

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Behaviorism focuses on observable behavior changes as a result of responding to stimuli, emphasizing reinforcement and punishment for behavior modification. It's key for environments that require a strong reinforcement of specific skills or behaviors.

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Cognitivism focuses on the mind's internal processes such as thinking, memory, knowing, and problem-solving. Cognitive theorists strive to understand how information is processed, stored, and used to influence learning.

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Constructivism posits that learners construct their own understanding and knowledge of the world, through experiencing things and reflecting on those experiences. It encourages active participation and discovery learning strategies.

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The ADDIE model, with its structured phases of Analysis, Design, Development, Implementation, and Evaluation, aligns well with Behaviorism by providing a systematic approach to creating structured learning environments where behaviors can be measured and reinforced through feedback.

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The ADDIE model is the generic process traditionally used by instructional designers and training developers. The five phases-Analysis, Design, Development, Implementation, and Evaluation-represent a dynamic, flexible guideline for building effective training and performance support tools. While perhaps the most common design model, there are a number of weaknesses to the ADDIE model which have led to a number of spin-offs or variations.

It is an Instructional Systems Design (ISD) model. Most of the current instructional design models are spin-offs or variations of the ADDIE model; other models include the Dick & Carey and Kemp ISD models. One commonly accepted improvement to this model is the use of rapid prototyping. This is the idea of receiving continual or formative feedback while instructional materials are being created. This model attempts to save time and money by catching problems while they are still easy to fix.

Instructional theories also play an important role in the design of instructional materials. Theories such as behaviorism, constructivism, social learning and cognitivism help shape and define the outcome of instructional materials.

In the ADDIE model, each step has an outcome that feeds into the subsequent step.

Analysis > Design > Development > Implementation > Evaluation

Analysis Phase

In the analysis phase, instructional problem is clarified, the instructional goals and objectives are established and the learning environment and learner's existing knowledge and skills are identified. Below are some of the questions that are addressed during the analysis phase:

* Who is the audience and their characteristics?
* Identify the new behavioral outcome?
* What types of learning constraints exist?
* What are the delivery options?
* What are the online pedagogical considerations?
* What is the timeline for project completion?

Design Phase

The design phase deals with learning objectives, assessment instruments, exercises, content, subject matter analysis, lesson planning and media selection. The design phase should be systematic and specific. Systematic means a logical, orderly method of identifying, developing and evaluating a set of planned strategies targeted for attaining the project's goals. Specific means each element of the instructional design plan needs to be executed with attention to details.

These are steps used for the design phase:

* Documentation of the project's instructional, visual and technical design strategy
* Apply instructional strategies according to the intended behavioral outcomes by domain (cognitive, affective, psychomotor).
* Create storyboards
* Design the user interface and user experience
* Prototype creation
* Apply visual design (graphic design)

Development Phase

The development phase is where the developers create and assemble the content assets that were created in the design phase. Programmers work to develop and/or integrate technologies. Testers perform debugging procedures. The project is reviewed and revised according to any feedback given.

Implementation Phase

During the implementation phase, a procedure for training the facilitators and the learners is developed. The facilitators' training should cover the course curriculum, learning outcomes, method of delivery, and testing procedures. Preparation of the learners include training them on new tools (software or hardware), student registration.

This is also the phase where the project manager ensures that the books, hands on equipment, tools, CD-ROMs and software are in place, and that the learning application or Web site is functional.

Evaluation Phase

The evaluation phase consists of two parts: formative and summative. Formative evaluation is present in each stage of the ADDIE process. Summative evaluation consists of tests designed for domain specific criterion-related referenced items and providing opportunities for feedback from the users.

Document courtesy of Wikipedia.org

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The ADDIE model is the generic process traditionally used by instructional designers and training developers. The five phases-Analysis, Design, Development, Implementation, and Evaluation-represent a dynamic, flexible guideline for building effective training and performance support tools. While perhaps the most common design model, there are a number of weaknesses to the ADDIE model which have led to a ...

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Gagne's Nine Events of Instruction is grounded in cognitive psychology, aligning with Cognitivism by designing instruction that enhances understanding and retention. Each event is meticulously planned to support information processing, ensuring learners encode, store, and retrieve information effectively.

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5. Provide learning guidance

Advise students of strategies to aid them in learning content and of resources available. In other words, help students learn how to learn.

The following are examples of methods for providing learning guidance:

Provide instructional support as needed - i.e. scaffolding that can be removed slowly as the student learns and masters the task or content

Model varied learning strategies - e.g. mnemonics, concept mapping, role playing, visualizing

Use examples and non-examples - examples help students see what to do, while non-examples help students see what not to do

Provide case studies, visual images, analogies, and metaphors - Case studies provide real world application, visual images assist in making visual associations, and analogies and metaphors use familiar content to help students connect with new concepts

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Gagne's Nine Events of Instruction | Center for Innovative Teaching and Learning | Northern Illinois University

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In 1965, Robert Gagné proposed a series of events that are associated with and address the mental conditions for learning.

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Problem-Based Learning (PBL) emphasizes learners' active role in constructing their own understanding by solving open-ended problems. This approach aligns with Constructivism, encouraging exploration, and reflection as learners apply their knowledge in real-world contexts.

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The Flipped Classroom utilizes technology to deliver instructional content outside of the classroom, allowing for active, participatory learning experiences in class. This strategy supports Constructivism by encouraging students to construct knowledge through exploration and application during in-class activities.

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Interactive storytelling allows students to become creators, exploring multiple story paths based on their decisions. This activity encourages creativity, critical thinking, and storytelling skills, aligning with Constructivism by enabling learners to construct their own narratives and outcomes.

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Concept maps are powerful tools for visualizing and organizing complex information, making them invaluable for educators seeking to understand the connections between different learning theories. When exploring behaviorism, cognitivism, constructivism, and connectivism on a concept map, it's essential to highlight their distinct principles and approaches to learning. Behaviorism focuses on observable behaviors and stimulus-response associations, cognitivism emphasizes mental processes such as memory and problem-solving, constructivism underscores the importance of active construction of knowledge through experiences, and connectivism acknowledges the role of networks and digital resources in knowledge acquisition.

Understanding a teacher's philosophy and preferred instructional strategies is crucial when presenting the concept map because it helps tailor the discussion to their specific needs and preferences. For instance, a teacher who aligns with behaviorism may appreciate concrete examples of behaviorist principles applied in the classroom, such as using reinforcement techniques in classroom management. On the other hand, a teacher embracing constructivism might be more interested in hearing about student-centered activities and project-based learning experiences. By catering to the teacher's unique perspective, the concept map becomes a personalized tool for professional development, fostering deeper insights and guiding instructional practices effectively.

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What are concept maps?

Concept maps are visual representations of information. They can take the form of charts, graphic organizers, tables, flowcharts, Venn Diagrams, timelines, or T-charts. Concept maps are especially useful for students who learn better visually, although they can benefit any type of learner. They are a powerful study strategy because they help you see the big picture: by starting with higher-level concepts, concept maps help you chunk information based on meaningful connections. In other words, knowing the big picture makes details more significant and easier to remember.

Concept maps work very well for classes or content that have visual elements or in times when it is important to see and understand relationships between different things. They can also be used to analyze information and compare and contrast.

Making and using concept maps

Making one is simple. There is no right or wrong way to make a concept map. The one key step is to focus on the ways ideas are linked to each other. For a few ideas on how to get started, take out a sheet of paper and try following the steps below:

Identify a concept.

From memory, try creating a graphic organizer related to this concept. Starting from memory is an excellent way to assess what you already understand and what you need to review.

Go through lecture notes, readings and any other resources you have to fill in any gaps.

Focus on how concepts are related to each other.

Your completed concept map is a great study tool. Try the following steps when studying:

Elaborate (out loud or in writing) each part of the map.

List related examples, where applicable, for sections of the map.

Re-create your concept map without looking at the original, talking through each section as you do.

Examples of concept maps

Example 1: This example illustrates the similarities and differences between two ideas, such as Series and Parallel Circuits. Notice the similarities are in the intersection of the 2 circles.

Example 2: This example illustrates the relationship between ideas that are part of a process, such as a Food Chain.

Example 3: This example illustrates the relationship between a main idea, such as climate change, and supporting details.

Example 4: Outlining is a less visual form of concept mapping, but it might be the one you're most familiar with. Outlining by starting with high-level course concepts and then drilling down to fill in details is a great way to determine what you know (and what you don't know) when you're studying. Creating an outline to start your studying will allow you to assess your knowledge base and figure out what gaps you need to fill in. You can type your outline or create a handwritten, color-coded one as seen in Example 5.

Additional study strategies

A concept map is one tool that you can use to study effectively, but there are many other effective study strategies. Check out these resources and experiment with a few other strategies to pair with concept mapping.

Make an appointment with an academic coach to practice using concept maps, make a study plan, or discuss any academic issue.

Attend a workshop on study strategies to learn about more options, get some practice, and talk with a coach.

How can technology help?

You can create virtual concept maps using applications like Mindomo, TheBrain, and Miro. You may be interested in features that allow you to:

Connect links, embed documents and media, and integrate notes into your concept maps

Search across maps for keywords

See your concept maps from multiple perspectives

Convert maps into checklists and outlines

Incorporate photos of your hand-written mapping

Testimonials

Learn more about how a Writing Center coach uses TheBrain to create concept maps in our blog post, TheBrain and Zotero: Tech for Research Efficiency.

Works consulted

Holschuh, J. and Nist, S. (2000). Active learning: Strategies for college success. Massachusetts: Allyn & Bacon.

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What are concept maps? Concept maps are visual representations of information. They can take the form of charts, graphic organizers, tables, flowcharts, Venn Diagrams, timelines, or T-charts. Concept maps are especially useful for students who learn better visually, although they ... Read more

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Connectivism is a learning theory that emphasizes the importance of networks and digital technologies in the learning process. It suggests that learning occurs through connections between people, information, and technology, with learners adapting and navigating these networks to access and apply knowledge effectively in an ever-changing digital landscape.

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Connectivism aligns well with instructional design models like Siemens' Connectivist Learning Design model and Merrill's First Principles of Instruction. Both emphasize leveraging networks, digital resources, and collaborative learning environments to facilitate knowledge creation and dissemination, reflecting connectivist principles of distributed cognition and learning through networks.

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Merrill’s Instructional Design Principles - Instructional Design Australia

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Merrill established five instructional design principles that can be applied. Instructional Design Australia use these principles in their designs.

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Drill and Practice aligns with behaviorism by providing immediate feedback and repetitive practice tailored to individual learner needs, fostering skill acquisition and reinforcement.

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Drill and practice is a behaviorist aligned technique in which students are given the same materials repeatedly until mastery is achieved. In each iteration, students are given similar questions to answer or activities to perform, with a certain percentage of correct responses or actions moving the student to the next level of difficulty.

Appropriate Content Areas

Most common in Kinesthetics, Coaching, Music, Mathematics, Language, Typing, and Biological Sciences. Not as common in adult education.

Examples

Anatomy Drill and Practice at the Student Companion site to Tortora & Grabowski, Introduction to the Human Body, 5th edition.

Goals and Objectives

Several goals can be attributed to drill and practice exercises. They can be used to build confidence as more answers are correctly provided. They also help to reinforce important materials. Learners are also provided and opportunity to practice critical skills and knowledge sets. Sample objectives are shown next.

During and after performing the activity, students will...

increase skill at performing the given task...

increase speed at performing the given task...

internalize the given information until it is an automatic assumption...

...as determined by successfully attending to 80% of rubric items.

Prerequisites

In general, there are no prerequisites for drill and practice. It is commonly used as a core set activity such as learning an alphabet. Prerequisites may exist if more advanced knowledge sets are to be practiced. For example, you would not use drill and practice in multiplication if the student does not already know addition.

Materials and Resources

The instructor must provide instruction in what is to be practiced. Depending on the curriculum, worksheets may be produced on which the students work.

Guiding Questions for this Lesson

There is not usually a guiding question in drill and practice exercises, perhaps adding to the opposition of its use in more advanced thinking skills. It is basically a technique used in process and declarative knowledge building activities.

Lesson Outline and Procedure

Before beginning a drill and practice exercise, the students must be informed and taught the underlying principles. Once the principle has been demonstrated or instructed, the students are given an activity, procedure, or worksheet to complete. A set time limit is usually employed. Assessment should quickly follow performance with opportunities for additional reinforcement and skill building. Effective use of drill and practice will depend on linking the activity to the actual skill that the instructor intends to develop.

Drill and practice activities can also be provided to students to do on their own time and at their own pace, with assessment provided after they have been completed. When appropriate, computer-based assessment can be used to provide immediate feedback without instructor overseeing the activity, such as in many mathematics drills.

Teaching Strategies

Kinesthetic drills should be in a real life situation.

Avoid criticisms of student ability. It may be that the prerequisites have not been met and the activity is not appropriate for the given student.

Finish every drill.

Studies have shown that children learn better individually rather than in pairs when performing drill and practice exercises.

Accommodations

What accommodations may be needed for students with disabilities or other special needs? Clearly, some physical disabilities will prohibit some kinesthetic activities from being performed. Time may also become a factor for some students, such as those with dyslexia, in performing some tasks since speed is often part of the assessment in drill and practice exercises.

Timeline

Drill and practice exercises are short term activities that are usually completed in under 10 minutes. There should be follow-up reinforcement at later times.

Ideas for Lesson Evaluation and Teacher Reflection

How did the students like the lesson? End of semester evaluations should ask about the usefulness and learning accomplished through such activities.

How was student learning verified? Participation can be assessed in discussion sessions. In general, student work is assessed by the number of correct responses or actions in a given time frame with process providing partial credit towards the final grade.

Additional Resources

Audioblox, (n.d.) Teaching myth: Repetition and drill dull creativity. Retrieved January 9, 2007, from http://www.audiblox2000.com/repetition.htm

Handal, B., & Herrington, A. (2003). Re-examining categories of computer-based learning in mathematics education.Contemporary Issues in Technology and Teacher Education[Online serial], 3(3). Retrieved January 9, 2007, from http://www.citejournal.org/vol3/iss3/mathematics/article1.cfm

Norman, D. A., & Spohrer, J. C. (1996). Learner-centered education.IT Forum, 12. Retrieved January 9, 2007, fromhttp://itech1.coe.uga.edu/itforum/paper12/paper12.html - "Rote learning and drill-and-practice are still essential to transform understanding into automated skill, making the information and procedures available to the mind without conscious effort.

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Drill and practice is a behaviorist aligned technique in which students are given the same materials repeatedly until mastery is achieved. In each iteration, students are given similar questions to answer or activities to perform, with a certain percentage of correct responses or actions moving the student to the next level of difficulty.

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Online Discussion Forums & Constructivism 

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Online Discussion Forums & Constructivism 

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Online Discussion Forums enable students to engage in collaborative discussions, share ideas, and co-construct knowledge in asynchronous online environments, supporting constructivist principles of social interaction and meaning-making.

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Social Media Integration & Connectivism 

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Social Media Integration & Connectivism 

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Social Media Integration allows learners to connect with a broader network of peers and experts, supporting connectivist principles of knowledge creation and dissemination through networked learning.

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Connectivism is a learning theory broadly based on networks and shared information within those networks. With technology, learning theories have moved into a digital age (Siemens, 2006) where learning in Web 2.0 enabled networks takes in shape of the connectivist principles. Concept of connectivism is driven by rapidly changing avenues of information that alter our decisions. And an individual's learning is constantly being affected by the outside - the community, networks or organization that individual is a part of.

This theory has largely changed the paradigm of how we view learning and progression of knowledge - it has moved it from a hierarchical structure as experienced from past centuries and morphed it into complex networks and mind maps that connect the individuals locally and globally. Learning is not limited to traditional movement e.g. education being acquired outside of a classroom with a plethora of resources available to learn from across these networks. For this, it becomes important as to first get where to find a piece of information across large databases than to answer the what and how of the knowledge.

Among these web 2.0 applications, social media networks allows for the most rapid collaboration across huge networks across the globe. The randomness and abundance of data exchanged/shared on social media takes the form of small organized pieces of information that propagate across the network and maneuvers our learning as actionable pieces of information. This further affects how decisions are made based on the foundations of learned information.

Social media and connectivism: Social media connects users to pieces of information that adds to their current set of knowledge. Social media largely works on relatable information propagation where influence emerges when user groups, knit together either by real life relations, relatability, mutual agreements on moving knowledge etc. Users are bombarded with knowledge about events, notions, news, updates, products, campaigns etc. that they interact with based on their selective likelihood of acquiring that information, basically creating their own "Personal Learning Environments, PLEs". So each of these users as an individual are starting points for connectivism. The set of knowledge contained within an individual connects back to the network it belongs to while adding or changing into the existing knowledge as it flows across the networks.

It is interesting to note how social media campaigning works in form of an unstructured, unbound (or say informal) way of learning where a user could either be actively or inactively engaged. In social media terms, we could categorize active participation in terms of engagements (shares, likes, retweets, replies). Inactive learning could be all the instances where no visible active engagement was present but the user viewed, scrolled through, clicked on or had second hand engagement (shared by a friend via personal message). In either case, the information learned about the campaign would have leave an impact based on: a) selective likelihood of acquiring that information by the user, b) spread of influence of campaign across networks (e.g. a trending hashtag).

To see how social media campaigns are working on underlying principles of connectivist learning, we study a local campaign in Pakistan called 'Aurat March' (Women's March) which began its momentum on social media platforms.

Background: A Pakistani women collective called Hum Auratein (We the Women) started an initiative named the Women's March which is a holistic campaign towards women rights, gender based discriminations and how the society as a whole needs to get rid of the shackles of patriarchy that has long ruled the nation. Women's March started their campaign solely on social media. Organizers in three large cities of Pakistan, Karachi, Islamabad and Lahore would conduct a yearly peaceful procession on International Women's day where various issues are raised from public harassment, rape violence, gender based violence, rights of marginal ethnic and gender groups etc.

We picked this campaign as it brought about a new phase of learning about the issues of women in Pakistan. Issues that were brushed under the carpet or hidden under the blanket of patriarchy, were brought about in daylight and rigorously talked about. This campaign proved influential over social networks as masses learned about issues, shared their stories, came out in support of injustice and violence, and is forcing the local society to learn and adapt into a new perspective of being part of this society. The campaign focuses on a structural and organizational change in what we have learned in the past to what we need to alter within our current knowledge.

Role of social networks: The promotion and success of this campaign has largely relied on information shared across social media networks. For this case study, we picked the twitter handles of the three cities' organizing groups. Since the campaign for this march started solely from social media networks, this is ideal to pick from the starting point - the organizer twitter profiles and individuals that rolled the ball for this campaign and how it leaves its influence as it moves across the network.

Studying the timeline over the last three years since its inception, saw the rise in engagements on twitter over three years. The first procession happened in Karachi only with a handful of women. By the third year, Women's March made rounds across the three major cities with a large set of women, men, children and people from marginalized communities coming out. It connected more and more women to the issues that pain. Friends, friends of friends, social media influencers, all connected together, sharing their stories, their views on improving the justice system, calling out patriarchal views and comments against the campaigns and the force keeps getting stronger as this initiative connected masses on a large scale.

The current analysis shows influence - tweet corpus of campaign organizers and measures the influence in terms of engagements. This leads to analyzing the other side - public opinion and response. In order to truly measure how effective a campaign had remained in information dissemination or as Aurat March's goal is, change of opinions and mindsets to focus more towards core issues faced by women.

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Connectivism theory and social media networks

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Connectivism is a learning theory broadly based on networks and shared information within those networks. With technology, learning theories have moved into a digital age (Siemens, 2006) where...

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Personal Learning Networks & Connectivism

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Personal Learning Networks & Connectivism

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Personal Learning Networks (PLNs) enable learners to curate and share resources, build personalized learning networks, and engage in self-directed learning, aligning with connectivist principles of learning within distributed networks.

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If you assume students are only learning from eLearning courses, you are failing to leverage a huge part of the learning process.

Ask yourself where do you learn?

Most of us might answer "in a classroom", but, in reality, the majority of learning comes from other places, like Personal Learning Networks. And these networks are personal, meaning there are no two PLNs that are equal; each of us has a unique range of people we learn from including peers, coworkers, supervisors, experts and other professionals. Today more learners use tools like Google and Wikipedia to acquire new skills and knowledge. They subscribe to personal and professional blogs. Or they ask questions through their social media networks to get the most relevant answers from the members of their PLN.

Defining PLN's

PLN stands for Personal Learning Network. It has a range of definitions but here is one that suits our purposes best: "A network of trusted connections with whom an individual interacts (and learns from) on a regular basis." - Jane Hart.

In other words, your PLN is where you gather, collect, communicate, create and also share knowledge and experience with a group of connected people, anywhere at any time. It is developed largely through social media, such as Twitter, LinkedIn, Facebook, and blogs, helping us form connections, grow our knowledge base and develop ourselves professionally through continual learning.

A PLN is further defined by intention. When someone makes a connection with the particular purpose of learning and gaining information from that person. This differs from merely meeting someone at random who you may form a friendship with. While you can have friends within your PLN, not all people within your PLN are people you would invite over for a BBQ or out to a bar.

PLNs are not new, but technology has, however, changed how we connect to others and have expanded their reach significantly. Social media and technology have, specifically, removed the physical barriers that once existed. Time zones and distance no longer present the significant hurdles they once did. With the multitude of social platforms and communication apps like Skype, GoToMeeting and Face Time there are very few things you can't do long distance.

Relationship Building through Technology

Even though we talk about technology with PLNs, they are much more about the connections and relationships we make.

According to EdWeek, to conceptualize a PLN, you should envision three layers, with ourselves at the center. The closest and smallest ring include friends and personal mentors. The next layer includes groups that share our interests but are not necessarily close friends. The final layers include experts, professionals, and celebrities. People who may not have much, if any personal interaction but who we still look up to and learn from.

What Does a PLN Do?

A PLN must serve three purposes in order to make other events in the network possible. These are:

Connection Building: a PLN adds new people to gain further knowledge resources for when they are needed.

Connection Maintenance: a PLN keeps you in touch with relevant people.

Connection Activation: a PLN activates connections with select people to allow for new learning. (Nardi, et al., 2000; Nardi, et al., 2002).

What a PLN is Not

PLNs and CoPs (Communities of Practice) are often used interchangeably; we could be forgiven for thinking that they are more or less synonymous. But, a PLN is not a CoP. With CoPs, the people who make up the community have a profession or professional field in common. A network though is markedly different in that it is made up of individuals who form individual connections that are not based on a single profession.

PLN connections are random and mostly informal, while CoP connections are more formal or structured since they require the common thread of professional expertise. CoPs are a resource about the shared professional field where members can give and take the info to benefit the group as a whole. PLNs are less focused, typically because they involve a much broader range of people with varying levels and kinds of expertise.

Further Reading:

PLN is not PLE

A PLE or Personal Learning Environment is not the same as a PLN either. PLEs are self-directed learning spaces, which contain tools to gather, organize and create knowledge that is specialized to an individual's goals and needs.

A PLN is a part of a PLE when a person has a group within their professional network that has a unifying factor such as common interest, project or goal. Social media and networking helps create these connections and furthers the spreading of information.

A PLE, in short, is much broader than a PLN including all the people in a PLN and the content that is generated through it. It is also more than the various tools that students use to research, write, access, and spread content with.

A PLE can be created when an eLearning course's VLE (Virtual Learning Environment) adds tools or applications that allow students to control their learning. So, in this way, a Personal Learning Environment is what comes about when a Virtual Learning Environment is personalized for users to direct themselves within the environment. Adding social elements where students can interact with each other within a PLE, turns it into a PLN.

Image Source: The Anatomy of a PLE

Why Building a PLN is Increasingly Important

The way we learn and interact in our current era of networking is defined and theorized upon by Connectivism. Connectivism shows us that a PLN is a person's way to get a continuous stream of knowledge and space to exchange thoughts and ideas while receiving validation of their contributions to the network and the challenges they rise to.

This is relevant to organizations because it is impossible to provide training for every single required skill that will be needed to perform a task or job. Training can only be provided for known duties but for all the unknowns, innovation and the ability to adapt, are the only ways to success. To makes this possible PLNs:

Give members several information sources and pathways.

Constructing your own PLN is a new way to develop yourself professionally with a network that caters to your needs and is available when you require it.

Help you stay up-to-date by providing the most current information from experts within your field

Provide a continuous stream of learning, resources and ways of thinking about topics that matter to you.

Help you find answers and advice when you need it because your online network always has someone available.

Increase your reach due to the technology involved in PLNs. Even persons who live and work remotely can make connections across the country or globe.

Allow you to gain information from things like workshops and conferences even when you are unable to physically attend them. Technology allows many of these learning events to be streamed live.

Expand your thinking by engaging in active discussion.

Acquire expert advice almost on demand since you are no longer limited to getting info from infrequently published texts and scholarly articles. Professionals and thought leaders in your field are continuously providing insights through their PLN and social media.

Want to start building your own PLN? Watch this video.

REFERENCES:

1. Learning in the Modern Workplace by Jane Hart. http://www.slideshare.net/janehart/learning-in-the-modern-workplace-43899391/19-mobile_content_mobile_rstmicro

2. I Believe in the Importance of a Personal Learning Network by Tom Spiglanin http://tom.spiglanin.com/2014/12/i-believe-in-the-importance-of-a-personal-learning-network/

3. Personal Learning Networks: Learning in a Connected World. ID and Other Reflections Blog http://idreflections.blogspot.com/2015/01/personal-learning-networks-for-ongoing.html

4. How to Create a Robust and Meaningful Personal Learning Network [PLN] by Online Learning Insights https://onlinelearninginsights.wordpress.com/2013/01/22/how-to-create-a-robust-and-meaningful-personal-learning-network-pln/

5. The Future of Work and Learning 1: The Professional Ecosystem by Jane Hart http://www.c4lpt.co.uk/blog/2016/05/08/the-professional-ecosystem-the-future-of-work-and-learning/

6. Learning Design and Personal Learning Networks by Joyce Seitzinger http://www.slideshare.net/catspyjamas/rmit-what-onearthaug2014

7. Seek, Sense, Share: Understanding the Flow of Information Through a Personal Learning Network http://www.lifewideeducation.uk/blog/category/personal-learning-networks

8. What is a PLN? Or, PLE vs. PLN? http://educationaltechnology.ca/couros/1156

9. Personal Learning Network Model http://firstmonday.org/ojs/index.php/fm/article/view/3559/3131

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If you assume students are only learning from eLearning courses, you are failing to leverage a huge part of the learning process. Ask yourself where do you learn? Most of us might answer "in a classroom", but, in reality, the majority of learning comes from other places, like Personal Learning Networks.

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Online Collaborative Projects & Connectivism

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Online Collaborative Projects & Connectivism

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Implementing online collaborative projects through platforms like Google Workspace or Microsoft Teams fosters connectivism by facilitating knowledge construction through social interaction and collaboration. Students engage in sharing ideas, resources, and perspectives, contributing to the collective knowledge network and leveraging diverse viewpoints to deepen understanding.

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What's The Connectivism Learning Theory?

Collaboration has been one of the main focal points in education in recent years, especially after the COVID-19 pandemic when people had to isolate themselves and lose physical connection with their peers. A relatively recent learning theory called connectivism suggests that students should gather ideas, theories, and general knowledge from different sources but combine them correctly. The theory was created in 2005 by two theorists, George Siemens and Stephen Downes, who believe that technology has changed the way we receive information, thus changing learning as well. The connectivism learning theory encourages group interaction and conversation, allowing us to express various opinions and positions when making choices, solving problems, and understanding data. Connectivism also encourages education through online communities, blogs, and other public spaces. Let's dive more into the theory and see how eLearning professionals can leverage it.

The Theory's Principles

Learning Is A Social Process

According to the theory, one doesn't learn individually but rather socially. This means that students learn more effectively when they interact with others, and they benefit from shared opinions, ideas, and experiences. They also feel freer to express their own thoughts. Therefore, the theory encourages students to actively participate in the learning process. As opposed to traditional learning, knowledge isn't delivered to students by the teacher but instead is created by them through their collaboration with others.

Culture And Society Influence Learning

The theory takes as a given that humans are social beings and that our beliefs are shaped through our culture and the society we live in, and so is our learning. What and how we learn are affected by the traditions, values, and customs of our community. So, we have to consider everyone's cultural and social environment when designing learning experiences.

Decisions Change Over Time

Our decisions change as our knowledge of everything around us changes. Especially in the digital world, where we receive large amounts of information at any given moment, our perspectives about things are altered the more we learn about them. In short, learning is powerful and shapes our opinions as we gain more knowledge.

Benefits Of The Connectivism Learning Theory

Collaboration

Since the goal is for students to enhance their knowledge with various opinions, they learn to collaborate with their peers. When they work together on projects, they benefit each other by sharing their ideas and offering solutions to a common problem. Additionally, with peer teaching, which involves students stepping into the teaching role, they develop important social skills that are useful in their future careers or academic pursuits.

Motivation

Students feel more excited about their lessons when they know they have more control over the content. Also, they enjoy it even more when participating in group activities with their peers. They are motivated to give it their all and actively engage with the content since they feel responsible for their own learning.

Diversity

Since the theory considers cultural and social backgrounds, it creates a learning environment where different experiences and beliefs are valued. Students have an equal opportunity to express their thoughts and opinions without biases based on their background. Most importantly, diverse perspectives enrich the lessons and cultivate a supportive environment.

How To Apply The Theory In eLearning

Online Discussion Forums

During online courses, learners may feel that they are disconnected from their peers or that they are not actively learning. To address this issue, you can incorporate online discussion forums and other communication tools for students to interact with each other and their instructors. Learners can use these tools to collaborate with their classmates, ask questions, and address challenges they may face.

Group Projects

Assign learners group projects and organize them into teams. Allow them to use any means of communication to work together, and highlight how important it is for everyone to contribute to the project. Even if every group member is in a different geographical location, they can bridge their distance thanks to modern technology, such as video conferencing platforms.

Service Learning

Service learning requires learners to apply their knowledge in real-world situations so that they understand the meaningfulness and importance of the lessons they attend. This process involves interactions between learners and organizations, aiming to solve common problems in their community. For example, instructors can contact local facilities and organizations to create programs where learners can work together to overcome challenges in their community, like traffic or safety.

Virtual Reality

Virtual Reality can create a virtual environment for learners to work together and apply their knowledge in situations that they might encounter in the real world. However, before implementing VR in your eLearning strategy, consider the potential barriers learners may have to deal with and train them on how to use the equipment.

Conclusion

Overall, the connectivism learning theory makes education learner-centered. It shifts the learning process's control over to the students, while teachers and instructors act as facilitators. Since the theory recognizes technology's important role in how we receive information and share knowledge, you can apply it in your online courses through collaborative strategies and meaningful material. If you want to discover other interesting Instructional Design models and theories, check out our list!

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Everything You Need To Know About The Connectivism Learning Theory

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Find out how the connectivism learning theory can transform traditional methods of learning, and how you can apply it in your online courses.

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Behavior-based Gamification This trend applies principles of behaviorism by using rewards and incentives to reinforce desired behaviors and learning outcomes. Educators can design gamified learning experiences where students earn points, badges, or rewards for achieving milestones or mastering content. This approach encourages active participation, motivates students to engage with course material, and provides immediate feedback, fostering a sense of accomplishment and agency.

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Educational Trend 2: Immediate Feedback Systems

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Educational Trend 2: Immediate Feedback Systems

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Immediate Feedback Systems Implementing immediate feedback systems aligns with behaviorist principles by providing students with timely responses to their actions or performance. Through digital platforms or tools, educators can offer instant feedback on assessments, quizzes, or activities, allowing students to adjust their learning strategies in real-time. This supports active learning by helping students identify areas for improvement, reinforcing correct behaviors, and promoting self-regulation and autonomy in the learning process.

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Educational Trend 1: Inquiry-Based Learning Platforms

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Educational Trend 1: Inquiry-Based Learning Platforms

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Inquiry-based learning platforms engage students in cognitive processes such as problem-solving, critical thinking, and knowledge construction. These platforms provide opportunities for students to explore complex topics, pose questions, conduct investigations, and develop deep conceptual understanding. By actively engaging in inquiry-based tasks, students take ownership of their learning, develop metacognitive skills, and construct meaningful connections between new information and prior knowledge.

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Educational Trend 2: Adaptive Learning Systems

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Educational Trend 2: Adaptive Learning Systems

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Adaptive learning systems leverage cognitive theories by personalizing instruction to meet individual student needs and learning preferences. These systems use algorithms to analyze student performance data and dynamically adjust the learning pathway, content difficulty, and instructional strategies. By adapting to each student's cognitive abilities and learning pace, educators can create tailored learning experiences that optimize content mastery, promote self-directed learning, and empower students to take control of their academic journey.

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Educational Trend 1: Project-Based Learning Environments

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Educational Trend 1: Project-Based Learning Environments

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Project-based learning environments immerse students in authentic, real-world tasks and challenges, fostering active construction of knowledge through hands-on experiences and collaboration. Students work together to investigate complex problems, design solutions, and create tangible artifacts that demonstrate their understanding. By engaging in project-based learning, students develop critical thinking skills, apply theoretical concepts to practical contexts, and construct deep conceptual understanding within meaningful learning contexts.

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Educational Trend 2: Collaborative Online Platforms

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Educational Trend 2: Collaborative Online Platforms

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Collaborative online platforms facilitate constructivist learning experiences by providing spaces for students to engage in cooperative inquiry, peer learning, and knowledge construction. Through features such as discussion forums, wikis, and shared documents, students collaborate with peers to co-create knowledge, share perspectives, and construct meaning collaboratively. These platforms promote active participation, social interaction, and collective sense-making, enabling students to explore diverse viewpoints, negotiate understanding, and take ownership of their learning process.

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Educational Trend 1: Social Learning Networks

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Educational Trend 1: Social Learning Networks

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Social learning networks leverage the principles of connectivism by connecting learners with diverse sources of information, expertise, and perspectives within digital communities and networks. Through platforms such as social media, online forums, and professional learning communities, students engage in networked learning experiences where they co-create knowledge, share resources, and collaborate with peers and experts globally. By participating in social learning networks, students develop digital literacy skills, cultivate personal learning networks, and navigate complex information environments in the digital age.

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Educational Trend 2: Personal Learning Environments (PLEs)

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Educational Trend 2: Personal Learning Environments (PLEs)

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Personal learning environments empower learners to curate, organize, and customize their digital learning spaces, tools, and resources according to their individual interests, goals, and preferences. By assembling a personalized ecosystem of digital tools, applications, and online resources, students construct their own learning pathways, pursue self-directed inquiry, and take ownership of their learning journey. PLEs support connectivist principles by promoting autonomy, flexibility, and learner agency, allowing students to access information, collaborate with peers, and engage in learning experiences tailored to their unique needs and interests.