Computerized Adaptive Testing Sources by Tori Lynn https://padlet.com/victoriasalefski/1i6soemtojq4lz0j Made with mirth en-us 2020-10-05 23:40:20 UTC 2023-05-29 04:24:11 UTC hello@padlet.com https://padlet.net/icons/png/1f4bb.png CAT to Personalize Instruction to Student Interests victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805013228 2020-10-05 23:42:37 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805013228 Progress Monitoring with CAT: The Impact of Data Collection Schedule on Growth Estimates victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805016514 2020-10-05 23:44:52 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805016514 Implementation of Computerized Adaptive Assessments victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805018809 2020-10-05 23:46:28 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805018809 Student Growth Measures: What We've Been Missing victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805020073 2020-10-05 23:47:23 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805020073 Routing Strategies and Optimizing Design for Multistage Testing in International Large-Scale Assessments victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805021459 2020-10-05 23:48:29 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805021459 Science Adaptive Assessment Tool: Kolb's Learning Style Profile and Student's Higher Order Thinking Skill Level victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805024570 2020-10-05 23:50:29 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805024570 Predicting Second-Grade Students' Yearly Standardized Reading Achievement Using a Computer-Adaptive Assessment victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805026147 2020-10-05 23:51:33 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805026147 Performance Verification Mechanism for Adaptive Assessment e-Platform and e-Navigation Application victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805031444 2020-10-05 23:54:40 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805031444 The Impact of Item Dependency on the Efficiency of Testing and Reliability of Student Scores from a Computer Adaptive Assessment off Reading Comprehension victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805037235 2020-10-05 23:58:30 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805037235 Computerized Adaptive Testing in Early Education: Exploring the Impact of Item Position Effects on Ability Estimation victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805048978 2020-10-06 00:06:05 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805048978 Automatic Question Tagging with Deep Neural Networks victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805056510 2020-10-06 00:10:27 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805056510 Dynamic Multistage Testing: A Highly Efficient and Regulated Adaptive Testing Method victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805059612 2020-10-06 00:12:13 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/805059612 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806338185 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.]]> 2020-10-06 12:14:42 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806338185 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806338651  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.]]> 2020-10-06 12:14:53 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806338651 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806340834 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.]]> 2020-10-06 12:15:46 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806340834 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806342081 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.]]> 2020-10-06 12:16:17 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806342081 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806342881 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.]]> 2020-10-06 12:16:37 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806342881 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806345244 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.]]> 2020-10-06 12:17:31 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806345244 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806345717 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
]]> 2020-10-06 12:17:43 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806345717 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806350270 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.]]> 2020-10-06 12:19:23 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806350270 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806351701 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]]> 2020-10-06 12:19:56 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806351701 Summary victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806353961 Talks about what Adaptive assessments are and benefits and limitations ]]> 2020-10-06 12:20:46 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806353961 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806358436 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.]]> 2020-10-06 12:22:22 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806358436 Abstract victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806359564 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
]]> 2020-10-06 12:22:46 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/806359564 victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/808628144 2020-10-06 23:32:12 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/808628144 arobinokanlawon https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811832974 2020-10-07 20:37:49 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811832974 ksolimani13 https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811843775 2020-10-07 20:42:36 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811843775 "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." maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811872381 2020-10-07 20:57:12 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811872381 maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811873792 2020-10-07 20:57:58 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811873792 maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811878398 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. ]]> 2020-10-07 21:00:38 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811878398 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). maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811909403 2020-10-07 21:17:43 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811909403 ksolimani13 https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811941150 2020-10-07 21:36:52 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/811941150 victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812076429 2020-10-07 23:10:11 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812076429 maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812102552 2020-10-07 23:29:25 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812102552 maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812107747 2020-10-07 23:33:01 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812107747 SAP maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812107859 The use of SAP of CAT to bring the teacher in]]> 2020-10-07 23:33:06 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812107859 maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812109986 2020-10-07 23:34:35 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812109986 Designing Effective Professional Development for Technology Integration in Schools victoriasalefski https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812127062 2020-10-07 23:46:04 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/812127062 arobinokanlawon https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/834102399 2020-10-15 22:01:18 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/834102399 arobinokanlawon https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/834205071 2020-10-15 23:15:40 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/834205071 Iceberg maura_cunningham https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/839338451 2020-10-18 23:15:38 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/839338451 arobinokanlawon https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/839358475 2020-10-18 23:37:27 UTC https://padlet.com/victoriasalefski/1i6soemtojq4lz0j/wish/839358475