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Nicole M. Hutchins; Gautam Biswas – British Journal of Educational Technology, 2024
This paper provides an experience report on a co-design approach with teachers to co-create learning analytics-based technology to support problem-based learning in middle school science classrooms. We have mapped out a workflow for such applications and developed design narratives to investigate the implementation, modifications and temporal…
Descriptors: Problem Based Learning, Teaching Methods, Science Instruction, Learning Analytics
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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
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Liu, Min; Cai, Ying; Han, Songhee; Shao, Peixia – Journal of Learning Analytics, 2022
Research on learning analytics (LA) has focused mostly at the university level. LA research in the K-12 setting is needed. This study aimed to understand 4,115 middle school students' learning paths based on their behavioural patterns and the relationship with performance levels when they used a digital learning game as their science curriculum.…
Descriptors: Learning Analytics, Navigation, Game Based Learning, Middle School Students
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Emily K. Toutkoushian; Kihyun Ryoo – Measurement: Interdisciplinary Research and Perspectives, 2024
The Next Generation Science Standards (NGSS) delineate three interrelated dimensions that describe what students should know and how they should engage in science learning. These present significant challenges for assessment because traditional assessments may not be able to capture the ways in which students engage with content. Science…
Descriptors: Middle School Students, Academic Standards, Science Education, Learner Engagement
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Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Interactive Learning Environments, 2024
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2)…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
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Liu, Min; Li, Chenglu; Pan, Zilong; Pan, Xin – Interactive Learning Environments, 2023
More research is needed on how to best use analytics to support educational decisions and design effective learning environments. This study was to explore and mine the data captured by a digital educational game designed for middle school science to understand learners' behavioral patterns in using the game, and to use evidence-based findings to…
Descriptors: Computer Games, Educational Games, Instructional Design, Instructional Effectiveness
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Lee, Ji-Eun; Chan, Jenny Yun-Chen; Botelho, Anthony; Ottmar, Erin – Educational Technology Research and Development, 2022
Online educational games have been widely used to support students' mathematics learning. However, their effects largely depend on student-related factors, the most prominent being their behavioral characteristics as they play the games. In this study, we applied a set of learning analytics methods (k-means clustering, data visualization) to…
Descriptors: Computer Games, Educational Games, Mathematics Instruction, Learning Processes
Lee, Ji-Eun; Chan, Jenny Yun-Chen; Botelho, Anthony; Ottmar, Erin – Grantee Submission, 2022
Online educational games have been widely used to support students' mathematics learning. However, their effects largely depend on student-related factors, the most prominent being their behavioral characteristics as they play the games. In this study, we applied a set of learning analytics methods ("k"-means clustering, data…
Descriptors: Computer Games, Educational Games, Mathematics Instruction, Learning Processes
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Grantee Submission, 2023
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction; and…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Grantee Submission, 2022
This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
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Sao Pedro, Michael; Jiang, Yang; Paquette, Luc; Baker, Ryan S.; Gobert, Janice – Grantee Submission, 2014
Students conducted inquiry using simulations within a rich learning environment for 4 science topics. By applying educational data mining to students' log data, assessment metrics were generated for two key inquiry skills, testing stated hypotheses and designing controlled experiments. Three models were then developed to analyze the transfer of…
Descriptors: Simulation, Transfer of Training, Bayesian Statistics, Inquiry
Hershkovitz, Arnon; Baker, Ryan S. J. d.; Gobert, Janice; Wixon, Michael; Sao Pedro, Michael – Grantee Submission, 2013
In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a "Discovery with Models" approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its…
Descriptors: Learning Analytics, Models, Learning Processes, Case Studies