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Designing Effective Professional Development for Technology Integration in Schools

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Designing Effective Professional Development for Technology Integration in Schools

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K-8 math games product that adapts to the learner's level of knowledge

Overview
Project Unicorn Vendor Signatory
DreamBox Learning teaches K-8 mathematics standards in an adaptive game format. Students play math games while the analytics system sources data points to adjust instruction instantly and provide reports for teachers, parents and administrators. Individual product licenses for schools and homes cost less than $20 per student.
APPROACH
DreamBox Learning delivers instruction via a web browser or free iPad app. Students with a school account can use DreamBox at home without the need to purchase an additional account or license. Accounts and individual progress sync across devices; students use the same account whether using a desktop, laptop or iPad at school or at home.

DreamBox Learning curriculum is aligned to national and regional mathematics standards in the USA and Canada. The curriculum aligns to Common Core State Standards for Mathematics (CCSSM) as well as the Texas Essential Knowledge and Skills (TEKS) and Virginia Public Schools Standards of Learning (SOL). In Canada, the curriculum is aligned to the Western and Northern Canadian Protocol Common Curriculum Framework for Mathematics. It's also aligned to the Ontario Curriculum used in the province of Ontario and surrounding areas.
DreamBox Learning for Students
When students log in to DreamBox, they choose a theme, character and storyline and immediately begin playing games. The program does not use a diagnostic, rather the system uses on-the-spot data to create a personalized learning plan that monitors each student's strengths and weaknesses, pace and problem-solving strategies. Based on a student's performance, the time allotment and scaffolding adjusts during the lesson. If a student repeatedly selects the wrong answer, they are prompted with hints to complete the task.

In addition to standards-aligned games, students can complete a variety of other activities both on and offline. As students complete game-based lessons, they receive tokens, which they can use to unlock additional games on the platform. Students can also print achievements and take-home worksheets with their favorite game characters.

In 2014, DreamBox began offering math games in Spanish.
DreamBox Learning for Teachers
Teachers have access to detailed student metrics such as time spent on the platform, competency in specific standards and progress towards module completion. In addition, the platform sends alerts to teachers when specific students perform poorly on a lesson or experience difficulty with a concept. The platform also suggests student groups based on proficiency level for differentiated instruction.
DreamBox Learning for Administrators
DreamBox Learning provides metrics for school and district administrators including school-specific summaries, concept mastery reports and trends in platform usage over time. The dashboard allows for class and grade comparisons to determine gaps in instruction or mastery for specific schools, grades, classes or students. DreamBox also allows schools to track where and how often licenses are being used to optimize their budget and facility usage.
PRICING
Home license:

One student: $12.95 monthly; $59.95 bi-annually

Up to four students: $19.95 monthly; $99.95 bi-annually

School license:

$20 per student, per year

$7,000 per school, per year (unlimited licenses)

RESEARCH
The What Works Clearinghouse (WWC) of the Institute of Education Sciences, US Department of Education evaluated an independent DreamBox Learning study by SRI International and concluded the program has a "potentially positive effect" on mathematics education. Specifically, the study compared two elementary student groups in which one group received DreamBox Learning instruction. The group receiving DreamBox Learning instruction had a higher overall math score on a mathematics skills test, though the extent of evidence was considered to be small.

Product Brief

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DreamBox Learning - Product Reviews - EdSurge

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Project Unicorn Vendor Signatory DreamBox Learning teaches K-8 mathematics standards in an adaptive game format. Students play math games while the analytics system sources data points to adjust instruction instantly and provide reports for teachers, parents and administrators. Individual product licenses for schools and homes cost less than $20 per student.

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Adaptive assessment and instruction for math and reading in grades K-8

Overview
Project Unicorn Vendor Signatory
The i-Ready software package delivers student instruction, performance diagnostics and progress reports based on K-12 Common Core State Standards (CCSS) in Mathematics and Reading. Students take the i-Ready Diagnostic and receive differentiated online instruction according to their ability, while schools and districts receive customized reports on student performance and progress throughout the year. Schools purchase licenses per subject, per student with discounts available for district-wide and multi-year packages.
APPROACH
i-Ready consists of a suite of four individual tools:

i-Ready Diagnostic - offers an adaptive diagnostic in K-12 Mathematics and Reading

i-Ready Online Instruction - K-8 targeted online instruction based on diagnostic

iPad apps - games that that support online instruction

i-Ready Standards Mastery - mini standards-based assessments

i-Ready for Students
Students begin by the program choosing a custom theme before taking the i-Ready subject diagnostic in Mathematics and Reading. The adaptive diagnostic varies in difficulty based on the student's previous answers: correct answers lead to more challenging questions and incorrect answers lead to easier questions. Immediately following the diagnostic, students work on customized online instruction that includes an interactive lesson, example problems and practice problems by a cast of colorful and diverse online characters. Students earn tokens for completing each lesson, which they can use to play games on the platform.

Throughout the year, the school can choose to offer additional diagnostics with i-Ready Standards Mastery.

In addition to online lessons, i-Ready has developed a series of drills-based iPad apps to complement online instruction. The apps fit into the lesson storyline and include vocabulary, multiplication and division facts, and abstract math. Parents can log into the platform to view student progress and scores on lessons.
i-Ready for Teachers
Teachers receive detailed reports on each student's diagnostic performance including their overall grade-level in Mathematics and Reading and grade-level for sub-skills in both subjects. Reading sub-skills include Phonological Awareness, Informational Text Comprehension and other language competencies. Math sub-skills include Measurement and Data and Geometry. The diagnostic assigns each student into a profile group based on their diagnostic grade level, which is useful for small group activities and delivering differentiated instruction.

Following the diagnostic, teachers can assign specific lessons to students based on a specific standard or sub-skill. Teachers can download lesson guides from i-Ready that complement individual online instruction.

On the i-Ready dashboard, reports offer insight into which students are below grade level, missing lessons or performing poorly on assessments. Individual student reports also include Lexile (reading level measures) and Quantile (math ability measures). The resources tab provides a user guide for the software and template letters for parents.
i-Ready for School and District Administrators
school and district professionals see a similar view as teachers, but with an expanded report features. District professionals can examine reports from the district level down to individual student records. The school and district specific reports include the number of students on target for grade-level as well as the number of at-risk students by school, grade or class. Schools and districts with multi-year licenses have the ability to perform year-over-year comparisons.
PRICING
Licenses for the i-Ready Diagnostic start at $6 per student, per subject, per year. Licenses for the combination of i-Ready Diagnostic and i-Ready instruction start at $30 per student, per subject, per year. Schools must purchase a minimum of 150 licenses. Schools that purchase district-wide or multi-year licenses may receive a discount. The i-Ready iPad apps are free on the iTunes store. Visit the website for detailed i-Ready pricing information.

Professional development training on how to use the software is available in online, blended and on-site formats. Packages average $2,500 to $3,500 per school site.
RESEARCH
An independent research study by the Educational Research Institute of America found that i-Ready Diagnostic scores predicted actual scores on New York State Math and English Language Arts assessments within one grade level with at least 95% accuracy. A similar independent study at Northwest Missouri State University confirmed that 3rd-grade students using i-Ready in Ohio public schools showed significant improvements on state tests in comparison with those who didn't use the program.

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Project Unicorn Vendor Signatory The i-Ready software package delivers student instruction, performance diagnostics and progress reports based on K-12 Common Core State Standards (CCSS) in Mathematics and Reading. Students take the i-Ready Diagnostic and receive differentiated online instruction according to their ability, while schools and districts receive customized reports on student performance and progress throughout the year.

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The expectancy-value model is also the framework most commonly used to conceptualizetest-taking motivation (e.g., Penk & Schipolowski, 2015), which is a particular type of achievement motivation. Theories of motivation have also acknowledged that in specific situations, taskcharacteristics (including test features) play an important role in determining motivation andbehavior (Vollmeyer & Rheinberg, 2006).

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The expectancy-value model is also the framework most commonly used to conceptualizetest-taking motivation (e.g., Penk & Schipolowski, 2015), which is a particular type of achievement motivation. Theories of motivation have also acknowledged that in specific situations, taskcharacteristics (including test features) play an important role in determining motivation andbehavior (Vollmeyer & Rheinberg, 2006).

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In particular, they argued that, since low-ability examinees will experience an easier test
(compared with a FIT), they may become less discouraged or disengaged during a CAT because
in a FIT, these lower ability examinees answer few items correctly.

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"When items are either very easy or very difficult for the test taker toanswer, the uncertainty about whether such an item can be answered correctly is low, and therefore, these items provide relatively little information about the examinee’s ability."

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"When items are either very easy or very difficult for the test taker toanswer, the uncertainty about whether such an item can be answered correctly is low, and therefore, these items provide relatively little information about the examinee’s ability."

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CAT to Personalize Instruction to Student Interests

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CAT to Personalize Instruction to Student Interests

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Progress Monitoring with CAT: The Impact of Data Collection Schedule on Growth Estimates 

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Progress Monitoring with CAT: The Impact of Data Collection Schedule on Growth Estimates 

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Student Growth Measures: What We've Been Missing 

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Student Growth Measures: What We've Been Missing 

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Routing Strategies and Optimizing Design for Multistage Testing in International Large-Scale Assessments 

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Routing Strategies and Optimizing Design for Multistage Testing in International Large-Scale Assessments 

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Science Adaptive Assessment Tool: Kolb's Learning Style Profile and Student's Higher Order Thinking Skill Level

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Science Adaptive Assessment Tool: Kolb's Learning Style Profile and Student's Higher Order Thinking Skill Level

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Predicting Second-Grade Students' Yearly Standardized Reading Achievement Using a Computer-Adaptive Assessment

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Predicting Second-Grade Students' Yearly Standardized Reading Achievement Using a Computer-Adaptive Assessment

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Performance Verification Mechanism for Adaptive Assessment e-Platform and e-Navigation Application 

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Performance Verification Mechanism for Adaptive Assessment e-Platform and e-Navigation Application 

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The Impact of Item Dependency on the Efficiency of Testing and Reliability of Student Scores from a Computer Adaptive Assessment off Reading Comprehension 

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The Impact of Item Dependency on the Efficiency of Testing and Reliability of Student Scores from a Computer Adaptive Assessment off Reading Comprehension 

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Computerized Adaptive Testing in Early Education: Exploring the Impact of Item Position Effects on Ability Estimation 

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Computerized Adaptive Testing in Early Education: Exploring the Impact of Item Position Effects on Ability Estimation 

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Automatic Question Tagging with Deep Neural Networks

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Automatic Question Tagging with Deep Neural Networks

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Dynamic Multistage Testing: A Highly Efficient and Regulated Adaptive Testing Method

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Dynamic Multistage Testing: A Highly Efficient and Regulated Adaptive Testing Method

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Computerized Adaptive Testing (CAT) is now widely used. However, inserting new items into the question bank of a CAT requires a great effort that makes impractical the wide application of CAT in classroom teaching. One solution would be to use the tacit knowledge of the teachers or experts for a pre-classification and calibrate during the execution of tests with these items. Thus, this research consists of a comparative case study between a Stratified Adaptive Test (SAT), based on the tacit knowledge of a teacher, and a CAT based on Item Response Theory (IRT). The tests were applied in seven Computer Networks courses. The results indicate that levels of "anxiety" expressed in the use of the SAT were better than those using the CAT, in addition to being simpler to implement. In this way, it is recommended the implementation of a SAT, where the strata are initially based on the tacit knowledge of the teacher and later, as a result of an IRT calibration.

Descriptors: Student Motivation, Adaptive Testing, Computer Assisted Testing, Item Response Theory, Anxiety, Computer Attitudes, Test Theory, Test Reliability, Test Validity, Comparative Analysis, Item Banks

Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://www.mii.lt/informatics_in_education/

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ERIC - EJ1195643 - An Adaptive Test Analysis Based on Students' Motivation, Informatics in Education, 2018

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Computerized Adaptive Testing (CAT) is now widely used. However, inserting new items into the question bank of a CAT requires a great effort that makes impractical the wide application of CAT in classroom teaching.

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Adaptive learning technologies are emerging in educational settings as a means to customize instruction to learners’ background, experiences, and prior knowledge. Here, a technology-based personalization

intervention within an intelligent tutoring system (ITS) for secondary mathematics was used to adapt instruction to students’ personal interests. We conducted a learning experiment where 145 ninth-grade Algebra I students were randomly assigned to 2 conditions in the Cognitive Tutor Algebra ITS. For 1 instructional unit, half of the students received normal algebra story problems, and half received matched problems personalized to their out-of-school interests in areas such as sports, music, and movies. Results showed that students in the personalization condition solved problems faster and more accurately within the modified unit. The impact of personalization was most pronounced for 1 skill in particular—writing symbolic equations from story scenarios—and for 1 group of students in particular—students who were struggling to learn within the tutoring environment. Once the treatment had been removed, students who had received personalization continued to write symbolic equations for normal story problems with increasingly complex structures more accurately and with greater efficiency. Thus, we provide evidence that interest-based interventions can promote robust learning outcomes—such as transfer and accelerated future learning—in secondary mathematics. These interest-based connections may allow for abstract

ideas to become perceptually grounded in students’ experiences such that they become easier to grasp. Adaptive learning technologies that utilize interest may be a powerful way to support learners in gaining fluency with abstract representational system

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Although extensive research exists on the use of curriculum‐based measures for progress monitoring, little is known about using computer adaptive tests (CATs) for progress‐monitoring purposes. The purpose of this study was to evaluate the impact of the frequency of data collection on individual and group growth estimates using a CAT. Data were available for 278 fourth‐ and fifth‐grade students. Growth estimates were obtained when five, three, and two data collections were available across 18 weeks. Data were analyzed by grade to evaluate any observed differences in growth. Further, root mean square error values were obtained to evaluate differences in individual student growth estimates across data collection schedules. Group‐level estimates of growth did not differ across data collection schedules; however, growth estimates for individual students varied across the different schedules of data collection. Implications for using CATs to monitor student progress at the individual or group level are discussed.

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Talks about what Adaptive assessments are and benefits and limitations

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The move toward assessments that measure student growth, rather than just proficiency, has been perceived as an improvement in state accountability systems. However, Michael Watson explains that, for many students, these measures present an incomplete picture. Because they are based on grade-level assessments, any growth achieved by students who are above or below grade level is lost. He recommends the use of adaptive assessments that incorporate material from multiple grade levels

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This study investigates the effect of several design and administration choices on item exposure and person/item parameter recovery under a multistage test (MST) design. In a simulation study, we examine whether number‐correct (NC) or item response theory (IRT) methods are differentially effective at routing students to the correct next stage(s) and whether routing choices (optimal versus suboptimal routing) have an impact on achievement precision. Additionally, we examine the impact of testlet length on both person and item recovery. Overall, our results suggest that no single approach works best across the studied conditions. With respect to the mean person parameter recovery, IRT scoring (via either Fisher information or preliminary EAP estimates) outperformed classical NC methods, although differences in bias and root mean squared error were generally small. Item exposure rates were found to be more evenly distributed when suboptimal routing methods were used, and item recovery (both difficulty and discrimination) was most precisely observed for items with moderate difficulties. Based on the results of the simulation study, we draw conclusions and discuss implications for practice in the context of international large‐scale assessments that recently introduced adaptive assessment in the form of MST. Future research directions are also discussed.

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This study aims to determine students’ profiles of learning styles, levels of higher-order thinking skills, and the effect of differences in students’ competence to various HOTS instruments using the Science Adaptive Assessment Tool application. In this study, researcher used the descriptive survey approach. The subjects of this study were 251 students of grade 8 (Al-Zahra Indonesia Secondary school and MTsN 1 South Tangerang (Islamic Secondary school) academic year of 2019/2020. The research instrument used was a test to measure 21st century skills (HOTS), which varied on the learning styles of students studying natural science (Biology and Physics). The instrument was validated by expert judgment and empirically tested in order to obtain instrument reliability of learning style with adequate to high category variations. The results show: (1) the profile of the most popular student learning styles is the assimilator (27,50%), while at least it is converger (20,71%); (2) Females tend to have assimilator learning style pattern, while males tend to have an accommodator learning style; (3) The higher-order

thinking skills level in the Biology material was moderate (an average score of 39,69 from a maximum score of 70). The physics subject is in the lower category (an average score of 21,28 from a maximum score of 70); (4) The achievement of the HOTS score was influenced by the type of learning style and had average of a very small correlation, (5) There was significant difference incompetence across the Kolb’s learning styles—divergers, as-similators, convergers, and accommodators with the use of various HOTS instruments

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The present study investigated the predictability of a computer-adaptive, curriculum-based reading assessment for measuring second-grade students’ overall and comprehension reading achievement on a standardized reading achievement test. Specifically, second-grade student scores (N = 428) of the Istation’s Indicators of Progress for Early Reading (ISIP-ER) and the Standardized Test for the Assessment of Reading (STAR Reading) in one state were examined. Linear regression analysis was conducted in Mplus to determine the predictability between the ISIP-ER and STAR Reading scores, identifying that both the ISIP-ER overall reading and comprehension scores in December predicted the standardized reading scores in Spring (May). Confidence intervals were estimated to identify the ISIP-ER cut scores that predict STAR Reading scores for all achievement levels. Overall, ISIP-ER shows promise as a universal screening tool for second-grade student yearly reading skills.

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Adaptive assessment e-platform is being promoted in the world to make teachers understand students’ e-learning performance on the Internet. However, system's load testing for an adaptive assessment is a very important issue during development of such an e-platform. In this paper, we have adopted the genetic fuzzy markup language (GFML) to infer the performance of an adaptive assessment e-platform. Firstly, we collected the data and information of the e-platform loading in two different mechanisms. With the collected data, the proposed CPU usage calculation mechanism is first implemented to acquire the CPU usage information from the screenshot of Ganglia. Next, we used the fuzzy c-means (FCM) clustering mechanism to construct the knowledge base according to the collected data. Then, number of threads, constant timer, MySQL parameter, CPU usage, and testing time of the e-platform were utilized to infer the e-platform load performance. Finally, the genetic learning algorithm was utilized to learn the knowledge and rule base to optimize the proposed approach. From these experimental results, the proposed method is feasible for verifying the performance of an adaptive assessment e-platform. In the future, the adaptive assessment e-platform can be utilized to e-Navigation systems and applications.

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The objective of the present study was to evaluate the extent to which students who took a computer adaptive test of reading comprehension accounting for testlet effects were administered fewer passages and had a more precise estimate of their reading comprehension ability compared to students in the control condition. A randomized controlled trial was used whereby 529 students in Grades 4–8 and 10 were randomly assigned to one of two conditions, both of whom took a computerized adaptive assessment of reading comprehension. Participants in the experimental condition had ability scores estimated as a function of an item response model, which accounted for item-dependence effects in the reading assessment, whereas control students took a version where item-dependence effects were not controlled. Results indicated that examinees in the experimental condition took fewer passages (average Hedges' g = 0.97) and had more reliable estimates of their reading comprehension ability (average Hedges' g = 0.60). Findings are discussed in the context of potential time savings in assessment practices without sacrificing reliability.

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Studies have shown that item difficulty can vary significantly based on the context of an item within a test form. In particular, item position may be associated with practice and fatigue effects that influence item parameter estimation. The purpose of this research was to examine the relevance of item position specifically for assessments used in early education, an area of testing that has received relatively limited psychometric attention. In an initial study, multilevel item response models fit to data from an early literacy measure revealed statistically significant increases in difficulty for items appearing later in a 20‐item form. The estimated linear change in logits for an increase of 1 in position was .024, resulting in a predicted change of .46 logits for a shift from the beginning to the end of the form. A subsequent simulation study examined impacts of item position effects on person ability estimation within computerized adaptive testing. Implications and recommendations for practice are discussed.

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In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students' ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging, an alternative, has not been studied extensively. In this paper, we propose a position-based attention model and keywords-based model to automatically tag questions with knowledge units. With regard to multiple-choice questions, the proposed models employ mechanisms to capture useful information from keywords to enhance tagging performance. Unlike traditional machine learning-based tagging methods, our models utilize deep neural networks to represent questions using contextual information. The experimental results show that our proposed models outperform some traditional classification and topic methods by a large margin on an English question bank dataset.

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This study introduced dynamic multistage testing (dy-MST) as an improvement to existing adaptive testing methods. dy-MST combines the advantages of computerized adaptive testing (CAT) and computerized adaptive multistage testing (ca-MST) to create a highly efficient and regulated adaptive testing method. In the test construction phase, multistage panels are assembled using similar design principles and assembly techniques with ca-MST. In the administration phase, items are adaptively administered from a dynamic interim pool. A large-scale simulation study was conducted to evaluate the merits of dy-MST, and it found that dy-MST significantly reduced test length while maintaining the identical classification accuracy with the full-length tests and meeting all content requirements effectively. Psychometrically, the testing efficiency in dy-MST was comparable to CAT. Operationally, dy-MST allows for holistic pre-administration management of test content directly at the test level. Thus, dy-MST is deemed appropriate for delivering adaptive tests with high efficiency and well-controlled content.

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The use of SAP of CAT to bring the teacher in