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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
Xiong, Jiawei; Li, Feiming – Educational Measurement: Issues and Practice, 2023
Multidimensional scoring evaluates each constructed-response answer from more than one rating dimension and/or trait such as lexicon, organization, and supporting ideas instead of only one holistic score, to help students distinguish between various dimensions of writing quality. In this work, we present a bilevel learning model for combining two…
Descriptors: Scoring, Models, Task Analysis, Learning Processes
Hai Li; Wanli Xing; Chenglu Li; Wangda Zhu; Simon Woodhead – Journal of Learning Analytics, 2025
Knowledge tracing (KT) is a method to evaluate a student's knowledge state (KS) based on their historical problem-solving records by predicting the next answer's binary correctness. Although widely applied to closed-ended questions, it lacks a detailed option tracing (OT) method for assessing multiple-choice questions (MCQs). This paper introduces…
Descriptors: Mathematics Tests, Multiple Choice Tests, Computer Assisted Testing, Problem Solving
Doewes, Afrizal; Saxena, Akrati; Pei, Yulong; Pechenizkiy, Mykola – International Educational Data Mining Society, 2022
In Automated Essay Scoring (AES) systems, many previous works have studied group fairness using the demographic features of essay writers. However, individual fairness also plays an important role in fair evaluation and has not been yet explored. Initialized by Dwork et al., the fundamental concept of individual fairness is "similar people…
Descriptors: Scoring, Essays, Writing Evaluation, Comparative Analysis
Savi, Alexander O.; Deonovic, Benjamin E.; Bolsinova, Maria; van der Maas, Han L. J.; Maris, Gunter K. J. – Journal of Educational Data Mining, 2021
In learning, errors are ubiquitous and inevitable. As these errors may signal otherwise latent cognitive processes, tutors--and students alike--can greatly benefit from the information they provide. In this paper, we introduce and evaluate the Systematic Error Tracing (SET) model that identifies the possible causes of systematically observed…
Descriptors: Learning Processes, Cognitive Processes, Error Patterns, Models
Ningsih, Tutuk; Yuwono, Dwi Margo; Sholehuddin, M. Sugeng; Suharto, Abdul Wachid Bambang – Journal of Social Studies Education Research, 2021
Learning at home not only provides written assignments that are changed in electronic form but must also reflect student learning outcomes at home. Likewise, researchers use literary reading to avoid students getting bored with learning Indonesian language literacy and character education. However, improving literacy skills is not just reading…
Descriptors: Indonesian, Computer Assisted Testing, Fiction, Literacy
Finkelstein, Idit; Soffer-Vital, Shira; Shraga-Roitman, Yael; Cohen-Liverant, Revital; Grebelsky-Lichtman, Tsfira – International Journal of Higher Education, 2022
Due to COVID-19, the world has encountered new challenges regarding pedagogy, learning, assessment, and evaluation. In meeting these challenges, there have been rapid changes in learning, and the gap between pedagogy and evaluation has grown. The purpose of this paper is to develop a new evaluative model suitable for the technologically enhanced,…
Descriptors: Student Evaluation, Evaluation Methods, Models, Culturally Relevant Education
Aust, Frederik; Haaf, Julia M.; Stahl, Christoph – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Evaluative conditioning (EC) is a change in liking of neutral conditioned stimuli (CS) following pairings with positive or negative stimuli (unconditioned stimulus, US). A dissociation has been reported between US expectancy and CS evaluation in extinction learning: When CSs are presented alone subsequent to CS-US pairings, participants cease to…
Descriptors: Memory, Conditioning, Decision Making, Learning Processes
Capacho, Jose – Turkish Online Journal of Distance Education, 2017
This paper aims at showing a new methodology to assess student learning in virtual spaces supported by Information and Communications Technology-ICT. The methodology is based on the Conceptual Pedagogy Theory, and is supported both on knowledge instruments (KI) and intelectual operations (IO). KI are made up of teaching materials embedded in the…
Descriptors: Student Evaluation, Computer Assisted Testing, Difficulty Level, Thinking Skills
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
Sharma, Manjula Devi; Bewes, James – Journal of Learning Design, 2011
Metacognition is the higher-order monitoring that deals with a person's regulation of thought processes and governs learning strategies and understanding in an instructional setting. The ability to appraise and judge the quality of one's own cognitive work in the course of doing it is self-monitoring. If the work needs to be done within a short…
Descriptors: Models, Academic Achievement, Learning Strategies, Self Esteem
Redhead, Edward S.; Hamilton, Derek A. – Learning and Motivation, 2009
Three computer based experiments, testing human participants in a non-immersive virtual watermaze task, used a blocking design to assess whether two sets of geometric cues would compete in a manner described by associative models of learning. In stage 1, participants were required to discriminate between visually distinct platforms. In stage 2,…
Descriptors: Experimental Groups, Control Groups, Cues, Learning Strategies
Tucker, Bill – Educational Leadership, 2009
New technology-enabled assessments offer the potential to understand more than just whether a student answered a test question right or wrong. Using multiple forms of media that enable both visual and graphical representations, these assessments present complex, multistep problems for students to solve and collect detailed information about an…
Descriptors: Research and Development, Problem Solving, Student Characteristics, Information Technology
Chu, Hui-Chun; Hwang, Gwo-Jen; Huang, Yueh-Min – Innovations in Education and Teaching International, 2010
Conventional testing systems usually give students a score as their test result, but do not show them how to improve their learning performance. Researchers have indicated that students would benefit more if individual learning guidance could be provided. However, most of the existing learning diagnosis models ignore the fact that one concept…
Descriptors: Test Results, Teaching Methods, Elementary School Students, Elementary School Teachers
Hodgson, Eric; Waller, David – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2006
Four experiments required participants to keep track of the locations of (i.e., update) 1, 2, 3, 4, 6, 8, 10, or 15 target objects after rotating. Across all conditions, updating was unaffected by set size. Although some traditional set size effects (i.e., a linear increase of latency with memory load) were observed under some conditions, these…
Descriptors: Experimental Psychology, Long Term Memory, Spatial Ability, Learning Processes
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