Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1

Ott, Claudia; Robins, Anthony; Haden, Patricia; Shephard, Kerry

doi:10.1080/08993408.2015.1033129

Cited by 36 publications

(22 citation statements)

References 19 publications

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“…These indicators include pre-course grades, number of submitted laboratory tasks, time of submission, and mid-semester exam result. These indicators were then visualized in the infographic they created for their course (Ott et al, 2015). Feild (2015) conducted exploratory data analysis in order to determine which indicators were worth reporting to students as feedback.…”

Section: Information Selectionmentioning

confidence: 99%

Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems

Bodily

Verbert

2017

IEEE Trans. Learning Technol.

233

158

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This article is a comprehensive literature review of student-facing learning analytics reporting systems that track learning analytics data and report it directly to students. This literature review builds on four previously conducted literature reviews in similar domains. Out of the 945 articles retrieved from databases and journals, 93 articles were included in the analysis. Articles were coded based on the following five categories: functionality, data sources, design analysis, student perceptions, and measured effects. Based on this review, we need research on learning analytics reporting systems that targets the design and development process of reporting systems, not only the final products. This design and development process includes needs analyses, visual design analyses, information selection justifications, and student perception surveys. In addition, experiments to determine the effect of these systems on student behavior, achievement, and skills are needed to add to the small existing body of evidence. Furthermore, experimental studies should include usability tests and methodologies to examine student use of these systems, as these factors may affect experimental findings. Finally, observational study methods, such as propensity score matching, should be used to increase student access to these systems but still rigorously measure experimental effects.

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Section: Information Selectionmentioning

confidence: 99%

Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems

Bodily

Verbert

2017

IEEE Trans. Learning Technol.

233

158

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show abstract

“…There were three good examples of articles that had justification for the information selection stage. Ott et al [2015] conducted a literature review to provide justification for the variables included in their reporting system [13]. Feild [2015] used exploratory analysis to identify which variables to include in their reporting system [14].…”

Section: Information Selectionmentioning

confidence: 99%

Trends and issues in student-facing learning analytics reporting systems research

Bodily

Verbert

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

113

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We conducted a literature review on systems that track learning analytics data (e.g., resource use, time spent, assessment data, etc.) and provide a report back to students in the form of visualizations, feedback, or recommendations. This review included a rigorous article search process; 945 articles were identified in the initial search. After filtering out articles that did not meet the inclusion criteria, 94 articles were included in the final analysis. Articles were coded on five categories chosen based on previous work done in this area: functionality, data sources, design analysis, perceived effects, and actual effects. The purpose of this review is to identify trends in the current studentfacing learning analytics reporting system literature and provide recommendations for learning analytics researchers and practitioners for future work.

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“…These crucial issues go to the core of teaching coding (Robins, Rountree, & Rountree, 2003;Watson & Li, 2014) and demand our attention given the growing need for coders across a broad range of careers as "seven million job openings in 2015 were in occupations which value coding skills" (Burning Glass, 2016, p. 3;Dishman, 2016;Thompson, 2018). The computer science education community has recognized the importance of better understanding students' performance in computer science courses for improving student outcomes (Alturki, 2016;Ott, Robins, Haden, & Shephard, 2015;Zingaro, 2015), and many have noted a bimodal distribution of grades in computer science (Corney, 2009;Dehnadi & Bornat, 2006;Robins, 2010). These researchers suggest that there are two distinct groups of computer science students, one stronger and one weaker, that can even be observed in distributions of learning outcomes in introductory computer science courses.…”

Section: Introductionmentioning

confidence: 99%

Exploring Bimodality in Introductory Computer Science Performance Distributions

Basnet

Payne

Doleck

et al. 2018

EURASIA J MATH SCI T

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This study examines student performance distributions evidence bimodality, or whether there are two distinct populations in three introductory computer science courses grades at a four-year southwestern university in the United States for the period 2014-2017. Results suggest that computer science course grades are not bimodal. These findings counter the double hump assertion and suggest that proper course sequencing can address the needs of students with varying levels of prior knowledge and obviate the double-hump phenomenon. Studying performance helps to improve delivery of introductory computer science courses by ensuring that courses are aligned with student needs and address preconceptions and prior knowledge and experience.

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Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1

Cited by 36 publications

References 19 publications

Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems

Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems

Trends and issues in student-facing learning analytics reporting systems research

Exploring Bimodality in Introductory Computer Science Performance Distributions

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