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Examining university teachers' self-regulation in using a learning analytics dashboard for online collaboration

EDUCATION AND INFORMATION TECHNOLOGIES 2024年第7期29卷 8523-8547页

作者： Huang, Lingyun Zheng, Juan Lajoie, Susanne P. Chen, Yuxin Hmelo-Silver, Cindy E. Wang, Minhong Educ Univ Hong Kong Fac Educ & Human Dev Dept Curriculum & Instruct Hong Kong Peoples R China Lehigh Univ Coll Educ Dept Educ & Human Serv Bethlehem PA USA McGill Univ Dept Educ & Counselling Psychol Montreal PQ Canada Univ S Alabama Mobile AL USA Indiana Univ Bloomington Sch Educ Bloomington IN USA Univ Hong Kong Fac Educ Hong Kong Peoples R China

learning analytics dashboards (LADs) are often used to display real-time data indicating student learning trajectories and outcomes. Successful use of LADs requires teachers to orient their dashboard reviews with clear goals, apply appropriate strategies to interpret visualized information on LADs and monitor and evaluate their interpretations to meet goals. This process is known as self-regulated learning (SRL). Critical as it is, little research investigates teachers' SRL in LAD usage. The present study addressed the gap by examining teachers' SRL and sought to understand how teachers' SRL relates to their use of LADs. To this end, a case study was designed in which ten participants were invited to use a LAD for asynchronous online problem-based learning. Think-aloud techniques and process mining methods were applied. The findings show that teachers were cognitive regulation in the early stage of LAD usage and became more metacognitive regulated later. The comparison of SRL between the good and the weak regulators indicates that the good self-regulators enacted more monitoring and evaluation events. Thus their regulator pattern was more non-linear. The qualitative analysis of think-aloud protocols reveals that teachers with good SRL are more likely to use the LAD to diagnose issues in student learning and collaboration. The study highlights the importance of SRL for teachers' success in using LAD for data-driven instructions. The study also reinforces the importance of fostering teachers' SRL, which accounts for teachers' professional success in the digital era.

关键词： learning analytics dashboard Self-regulated learning process Think-aloud protocols Process mining

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Meta-LAD: Developing a learning analytics dashboard with a Theoretically Grounded and Context-specific Approach 9

Meta-LAD: Developing a Learning Analytics Dashboard with a T...

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9th Annual IEEE learning with Massive Open Online Courses Conference (IEEE LWMOOCS)

作者： Choi, Heeryung Borrella, Inma Ponce-Cueto, Eva MIT Ctr Transportat & Logist Cambridge MA 02139 USA

ISBN: (纸本)9798350315592

The use of learning analytics dashboards (LADs) has gained popularity as a means of supporting the self-regulated learning (SRL) skills of learners in large-scale online courses. Despite many studies proposing LAD designs, LADs are often criticized for their weak theoretical foundations, lack of actionable feedback, and tendency to encourage excessive social comparison. Furthermore, many LAD designs have missed context-specific details. Hence, it is not uncommon for some dashboard designs to have negative effects on learners, such as discouragement or anxiety. In this study, we designed the Meta-LAD, a LAD that supports SRL processes using theoretical and contextual foundations. We used data from a credit-bearing Massive Open Online Course (MOOC) on supply chain management to contextually ground the dashboard. We performed usability testing interviews to evaluate the design and confirmed that the Meta-LAD could fulfill learners' needs for references and actionable feedback. This study contributes to the field of online learning by presenting a theoretically grounded and contextually specific LAD design process. This paper expands the understanding of how to support SRL in MOOCs.

关键词： MOOC learning analytics dashboard Self-regulated learning

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A Student-Centered learning analytics dashboard Towards Course Goal Achievement in STEM Education 18th

A Student-Centered Learning Analytics Dashboard Towards Cour...

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18th European Conference on Technology Enhanced learning (EC-TEL)

作者： Buvari, Sebastian Viberg, Olga Iop, Alessandro Romero, Mario KTH Royal Inst Technol EECS Stockholm Sweden

ISBN: (纸本)9783031426810;9783031426827

Online learning has become an everyday form of learning for many students across different disciplines, including STEM subjects in the setting of higher education. Studying in these settings requires students to self-regulate their learning to a higher degree as compared to campus-based education. A vital aspect of self-regulated learning is the application of goal-setting strategies. Universities act to support students' goal-setting through the achievement of course learning outcomes, which work both as a promise and metric of academic achievement. However, a lack of clear integration between course activities and course learning outcomes leaves a dissonance between students' study efforts and the course progress. This demo study presents a student-centered learning analytics dashboard aimed at assisting students in their achievement of course learning goals in the setting of STEM higher education. The dashboard was designed using a design science methodological approach. Thirty-seven students have contributed to its development and evaluation during different stages of the design process, including the conceptual iterative design and prototyping. The preliminary results show that students found the tool to be easy to use and useful for the achievement of the course goals.

关键词： learning Outcomes learning analytics dashboard STEM Higher Education Participatory Design

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The influence of learning analytics dashboard information design on cognitive load and performance

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年第15期29卷 19729-19752页

作者： Cheng, Nuo Zhao, Wei Xu, Xiaoqing Liu, Hongxia Tao, Jinhong Northeast Normal Univ Sch Informat Sci & Technol Changchun Jilin Peoples R China

learning analytics dashboards are becoming increasingly common tools for providing feedback to learners. However, there is limited empirical evidence regarding the effects of learning analytics dashboard design features on learners' cognitive load, particularly in digital learning environments. To address this gap, we developed goal-based, explanatory, and instructional learning analytics dashboards in authentic online courses based on cognitive load theory, and evaluated the effects of the three information designs on cognitive load and performance. The study adopted a quasi-experimental approach over a semester-long course, involving 93 learners divided into four groups, each provided with differently designed information on their learning analytics dashboard. The results show that the incorporation of goals, explanations, and instructional information as support elements in the learning analytics dashboard did not have a significant impact on learners' cognitive load and performance. Both cognitive load and learning performance results were consistent and mutually validating. Additionally, the study found that compared to a control group without additional information, the group using the explanatory dashboard experienced an increase in germane cognitive load, and evidenced the effectiveness of explanatory information design. Overall, this study provides important insights for the enhancement and practical design of learning analytics dashboards and feedback methods.

关键词： learning analytics dashboard Information design Cognitive load learning performance

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Comparison with Self vs Comparison with Others: The Influence of learning analytics dashboard Design on Learner dashboard Use 11th

Comparison with Self vs Comparison with Others: The Influenc...

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11th International Conference on Games and learning Alliance (GaLA)

作者： Gallagher, Timothy Slof, Bert van der Schaaf, Marieke Toyoda, Ryo Tehreem, Yusra Fracaro, Sofia Garcia Kester, Liesbeth Univ Utrecht Utrecht Netherlands Netherlands Inst Curriculum Dev Enschede Netherlands Univ Med Ctr Utrecht Utrecht Netherlands Newcastle Univ Newcastle Upon Tyne Tyne & Wear England Univ Appl Sci Emden Leer Emden Germany Bielefeld Univ Bielefeld Germany Merck KGaA Darmstadt Germany Katholieke Univ Leuven Darmstadt Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9783031221231;9783031221248

This study uses log-file data to investigates how chemical process plant employees interact and engage with two distinct learning analytics dashboard designs, which are implemented in a virtual reality simulation-based training environment. The learning analytics dashboard designs differ by reference frame: the progress reference frame, offers historical performance data as a point of comparison and the social reference frame offers aggregated average peer group performance data as a point of comparison. Results show that participants who receive a progress reference frame are likely to spend less time reviewing their dashboard than those who receive a social reference. However, those who receive a progress reference frame are more likely to spend more time reviewing detailed task feedback and engaging with the learning analytics dashboard.

关键词： learning analytics dashboard Social comparison Virtual reality simulation-based training

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Designing a learning analytics dashboard to Provide Students with Actionable Feedback and Evaluating Its Impacts 14

Designing a Learning Analytics Dashboard to Provide Students...

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14th International Conference on Computer Supported Education (CSEDU)

作者： Duan, Xiaojing Wang, Chaoli Rouamba, Guieswende Univ Notre Dame Notre Dame IN 46556 USA Purdue Univ Ft Wayne Ft Wayne IN USA

ISBN: (纸本)9789897585623

Various educational settings have begun to increasingly leverage the power of data analytics to optimize the learning environment and enhance the learning experience for students. However, despite this effort, significant research gaps still exist around utilizing educational data mining to provide students with actionable feedback and assess the comprehensive impact of data-informed feedback on students. In this study, a learning analytics dashboard was designed to provide students with actionable feedback to advance their self-regulated learning skills and improve their course performance. A rigorous inquiry using mixed methods was also conducted to study the dashboard's impacts on students. It found that students' use of the dashboard was positively correlated with their course performance, and those who viewed the dashboard had higher course ranks. In addition, it showed that students' use of the dashboard was positively correlated with their homework submission time, and those who viewed the dashboard submitted homework earlier as the course progressed. The inquiry also revealed that students had mixed feelings about the dashboard, including motivation and anxiety.

关键词： learning analytics dashboard Machine learning Self-regulated learning Actionable Feedback

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Development and Evaluation of a learning analytics dashboard for Moodle learning Management System 24th

Development and Evaluation of a Learning Analytics Dashboard...

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24th International Conference on Human-Computer Interaction (HCII)

作者： Peraic, Ivan Grubisic, Ani Univ Zadar Dept Informat Sci Zadar Croatia Univ Split Fac Sci Split Croatia

ISBN: (纸本)9783031221309;9783031221316

learning analytics provides a potential for adapting learning, teaching and knowledge testing processes to individual needs. One of the ways of using learning analytics is a dashboard for providing feedback to students and teachers. This paper presents the development and evaluation of the learning analytics dashboard for students (LAD-S). The LAD-S displays three views: a look at student success, system activities and prediction based on machine learning algorithms. We have used LAD-S as a part of Moodle online courses, one during the second semester in the 2020/2021, and the other two during the first semester in the 2021/2022. A survey was designed to examine students' opinion about the LAD-S that included student's self-awareness, influence of the dashboard on learning effectiveness, satisfaction with the type of data collected, usefulness and ease-of-use, intention to use the learning analytics dashboard. Data from 33 undergraduate and graduate students were collected. The results have shown that students are satisfied with all examined aspects of the LAD-S above the average. Students express the greatest satisfaction for ease of use (M = 3.79), clarity of collected data (M = 3.6), usefulness (M = 3.6), SUS questionnaire ( M = 3.6), behavioral intention (M = 3.4) and satisfaction with individual functions of LAD-S (M= 3.4). Lower, yet above-average satisfaction was obtained for the impact of the LAD-S on more effective learning (M = 3.2);intention to use (M = 3.3) and satisfaction with the possibility of behavioral changes (M = 3.1). To verify the reliability of the measures used, the Cronbach's alpha reliability coefficient was calculated for each scale. Satisfactory reliability of all measures used was obtained, with alpha coefficients ranging from 0.704 for the SUS questionnaire to 0.942 for the ease-of-use measure.

关键词： learning analytics learning analytics dashboard Evaluation

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Student-facing learning analytics dashboard: Profiles of Student Use

Student-facing Learning Analytics Dashboard: Profiles of Stu...

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IEEE Frontiers in Education Conference (FIE)

作者： Eickholt, Jesse Weible, Jennifer L. Teasley, Stephanie D. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA Cent Michigan Univ Dept Teacher Educ & Profess Dev Mt Pleasant MI 48859 USA Univ Michigan Sch Informat Ann Arbor MI 48109 USA

ISBN: (数字)9781665462440

ISBN: (纸本)9781665462440

Student-facing learning analytics dashboards (LADs) provide visualizations of course-related information to help students understand and personalize their educational practices. As such, they can be viewed as a meta-cognitive tool that enables awareness, self-reflection and sensemaking of academic performance. While student-facing LADs are becoming a standard feature in educational software, questions have been raised about students' willingness to adopt LADs and their ability to interpret feedback provided by student-facing LADs. The extent to which student-facing LADs can broadly improve educational outcomes depends, in part, on students' ability to readily incorporate LAD usage in their educational workflows. This study investigates the use of a student-facing LAD, My learning analytics (MyLA), over the span of one semester in a university introductory science course. MyLA draws data from the campus learning management system (Canvas) and displays three visualizations designed to provide students with actionable information. Adoption and use of MyLA was voluntary. As an exploratory study of MyLA's use in an introductory science course, this work addresses three research questions: i) What are the characteristics of students that use MyLA?, ii) How do students make use of MyLA in their coursework?, and iii) What patterns of use are exhibited by more frequent MyLA users? The results indicate that given the opportunity to use a student-facing LAD, 33% of students made repeated use of the tool. Demographic data (e.g., gender, domestic/international student) did not predict MyLA usage but significant differences in mean cumulative GPA were found between non-MyLA users and MyLA users. Broad patterns of MyLA use were aligned with major assessments in the course (e.g., MyLA was used more often around exam dates) and the grade distribution view was the most commonly accessed. Among the most highly active MyLA users, two distinct profiles were identified: aware and sensema

关键词： learning analytics dashboard student-facing learning analytics dashboard self-regulated learning course level micro-learning analytics

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Towards developing a learning analytics dashboard for a massive online robotics competition 13

Towards developing a learning analytics dashboard for a mass...

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13th IEEE Global Engineering Education Conference (IEEE EDUCON)

作者： Kodumuru, Saketh Lucas, Brendan Sabanwar, Vivek Patil, Sachin Avudiappan, Deepa Parikh, Parth Arya, Kavi Sastra Univ Comp Sci & Engn Thanjavur India Fr Conceicao Rodrigues Coll Engn Comp Engn Mumbai Maharashtra India Indian Inst Technol Dept Comp Sci & Engn Mumbai Maharashtra India

ISBN: (纸本)9781665444347

A learning analytics dashboard is a quick and efficient way for instructors to track the activities of students. In massive online learning scenarios like an international robotics competition, a dashboard is a critical tool for instructors to ensure continuous engagement of participants. Previous research on learning analytics dashboards focused on the effectiveness of dashboards and learning analytics on students along with factors affecting its success. This research discusses a dashboard developed for a massive robotics competition through which each year thousands of students are trained in engineering skills in an online Project Based learning approach. The dashboard is developed using the dataset for the competition conducted during September 2020 to April 2021 in which more than 10,000 undergraduate students from 572 academic institutions across 7 countries participated. Team characteristics like demographics, feedback, scores, online activity, etc. are considered to cluster teams and develop models to predict the retention of participants. The Machine learning (ML) model was able to achieve an accuracy of 80.7% and a recall value of 83.9% to identify dropping teams. Clustering provided insights on how these characteristics affected the performance of participants. These predictions along with participant engagement and feedback data was displayed on the dashboard. This visualization helps instructors identify teams requiring guidance or scaffolds to continue participation. Feedback from instructors shows the dashboard to be a promising tool for effectively managing massive online competitions.

关键词： learning analytics learning analytics dashboard Machine learning in learning analytics Project Based learning Engineering Education

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learning analytics dashboard for Problem-based learning 20

Learning Analytics Dashboard for Problem-based Learning

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7th Conference on learning at Scale

作者： Pan, Zilong Li, Chenglu Liu, Min Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781450379519

This study examined two machine learning models for designing a learning analytics dashboard to assist teachers in facilitating problem-based learning. Specifically, we used BERT to automatically process a large amount of textual data to understand students' scientific argumentation. We then used Hidden Markov Model (HMM) to find students' cognitive state transition with time-series data. Preliminary results showed the models achieved high accuracy and were coherent with related theories, indicating the models can provide teachers with interpretable information to identify in-need students.

关键词： learning analytics dashboard artificial intelligence machine learning problem-based learning (PBL)

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