Identifying computer science self-regulated learning strategies

Falkner, Katrina; Vivian, Rebecca; Falkner, Nickolas

doi:10.1145/2591708.2591715

Cited by 101 publications

(46 citation statements)

References 19 publications

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“…Not all students are naturally good at regulating their learning. They overestimate their skills [18], use few metacognitive strategies unless explicitly scaffolded [19], [20], [13] and may not use metacognitive aids even if such are provided [21]. This suggests that lack of self-regulation is a source of underachievement and it is important to investigate ways to facilitate it in the context of online learning environments where students are required to independently take responsibility of their working.…”

Section: Introductionmentioning

confidence: 99%

Increasing Students’ Awareness of Their Behavior in Online Learning Environments with Visualizations and Achievement Badges

Auvinen

Hakulinen

Malmi

2015

IEEE Trans. Learning Technol.

139

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In online learning environments where automatic assessment is used, students often resort to harmful study practices such as procrastination and trial-and-error. In this paper, we study two teaching interventions that were designed to address these issues in a university-level computer science course. In the first intervention, we used achievement badges, a form of gamification, to reward students for submitting early, solving exercises with few iterations, and completing the exercises with full points. In the second intervention, we used heatmap visualizations that show a prediction of the student's success if he or she continues to behave in the same way, based on data from earlier courses. The results from a controlled experiment show that the heatmap visualizations had an impact on how early the students submitted their exercises and how many points they earned from them. We also observed that performance approach goal orientation correlated with an interest towards the achievement badges whereas performance avoidance correlated with an interest towards the visualizations. The results indicate that increasing students' awareness of their behavior can have a positive impact on their study practices and results. However, the same type of feedback may not suit all students because of individual differences.

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Section: Introductionmentioning

confidence: 99%

Increasing Students’ Awareness of Their Behavior in Online Learning Environments with Visualizations and Achievement Badges

Auvinen

Hakulinen

Malmi

2015

IEEE Trans. Learning Technol.

139

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“…Read the names of all of the functions and variables in the program 18 # Some programs produce command line output with print statements. 19 # Is the faulty output you're investigating printed to a command line? 20…”

Section: Programming Strategiesmentioning

confidence: 99%

Explicit programming strategies

et al. 2020

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Software developers solve a diverse and wide range of problems. While software engineering research often focuses on tools to support this problem solving, the strategies that developers use to solve problems are at least as important. In this paper, we offer a novel approach for enabling developers to follow explicit programming strategies that describe how an expert tackles a common programming problem. We define explicit programming strategies, grounding our definition in prior work both within software engineering and in other professions which have adopted more explicit procedures for problem solving. We then present a novel notation called Roboto and a novel strategy tracker tool that explicitly represent programming strategies and frame executing strategies as a collaborative effort between human abilities to make decisions and computer abilities to structure process and persist information. In a formative evaluation, 28 software developers of varying expertise completed a design task and a debugging task. We found that, compared to developers who are free to choose their own strategies, developers given explicit strategies experienced their work as more organized, systematic, and predictable, but also more constrained. Developers using explicit strategies were objectively more successful at the design and debugging tasks. We discuss the implications of Roboto and these findings, envisioning a thriving ecosystem of explicit strategies that accelerate and improve developers' programming problem solving.

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“…Students' learning impediment and high dropout rate can also be caused by their inadequate self-regulation (SR) skills. Falkner, Vivian, & Falkner (2014) reported some students are incapable of considering and addressing the assessment criteria during their problem-solving enterprise. Peng, Wang, & Sampson (2017) reported students are unable to identify the problem goal and expected output correctly.…”

Section: Introductionmentioning

confidence: 99%

Do Computer Science Students Understand Their Programming Task?—A Case Study of Solving the Josephus Variant Problem

Febrian¹,

Lawanto²

2018

IES

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The ability of students to problem solve begins with interpreting the problem. When they interpret the problem inaccurately, they will likely use ineffective strategies or fail to solve the problem. Studies reported students are often incapable of identifying and articulating the problem goal, requirements/constraints, and expected output. In other words, students lack self-regulation skills, especially related to task understanding. In this study, two male and two female senior computer science students from Utah State University, USA, were recruited as research participants to learn more about their task understanding skills while engage in programming tasks. The participants were asked to answer five programming problems while thinking aloud, and their responses were video- and audio-recorded. This report focuses on one of the problems, which was a variant of the Josephus problem. Three research questions were used to guide the analysis: (a) what were the participants’ initial task understanding; (b) how did it change during the problem-solving endeavor; and (c) why did it change. All participants identified the problem goal inaccurately and as a result, selected ineffective problem-solving strategies. The analysis suggested their inaccurate task interpretations were caused by their confidence bias (i.e., a systematic cognitive error), in which they drew knowledge and strategies from irrelevant experience. Out of four participants, only one was able to defeat the confidence bias and acquired an accurate task understanding; the influencing factors and possible interventions to overcome confidence bias are discussed.

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Identifying computer science self-regulated learning strategies

Cited by 101 publications

References 19 publications

Increasing Students’ Awareness of Their Behavior in Online Learning Environments with Visualizations and Achievement Badges

Increasing Students’ Awareness of Their Behavior in Online Learning Environments with Visualizations and Achievement Badges

Explicit programming strategies

Do Computer Science Students Understand Their Programming Task?—A Case Study of Solving the Josephus Variant Problem

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