Adaptive Immediate Feedback Can Improve Novice Programming Engagement and Intention to Persist in Computer Science

Marwan, Samiha; Gao, Ge; Fisk, Susan R.; Price, Thomas W.; Barnes, Tiffany

doi:10.1145/3372782.3406264

Cited by 69 publications

(22 citation statements)

References 42 publications

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“…Concerning the challenge P1 (mixed-ability students), ScratchThAI employs the learner-centered approach and personalized learning approach to address the challenge where students are able to learn at their own knowledge level, pace and style (Bjork and Bjork 2011;Deunk et al 2018;OECD 2012;Schleicher 2016;Peng et al 2019). Personalized & Adaptive Technology (T1), and Automatic CT Assessment (T2) are the key enabling technologies that can help improve students' progress and engagement (Campos et al 2012;Hattie and Timperley 2007;Marwan et al 2020).…”

Section: Research Design Overviewmentioning

confidence: 99%

ScratchThAI: A conversation-based learning support framework for computational thinking development

2022

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Computational Thinking (CT) has been formally incorporated into the National Curriculum of Thailand since 2017, where Scratch, a block-based visual programming language, has been widely adopted as CT learning environment for primary-level students. However, conducting hands-on coding activities in a classroom has caused substantial challenges including mixed-ability students in the same class, high student-teacher ratio and learning-hour limitation. This research proposes and develops ScratchThAI as a conversation-based learning support framework for computational thinking development to support both students and teachers. More specifically, it provides learning experiences tailored to individual needs. Students can learn CT concepts and practice online coding anywhere, anytime. Moreover, through its ScratChatbot, students can ask for CT concept explanations, coding syntax or practice exercises. Additional exercises may be assigned to students based on the diagnosed individual learning difficulties in a particular topic to provide possible and timely intervention. Teachers can track learning progress and performance of the whole class as well as of individuals through the dashboard and can take suitable intervention within limited school hours. Deploying ScratchThAI to several Thai schools has enabled this research to investigate its effectiveness in a school setting. The obtained results indicated positive teacher satisfaction, better learning performance and higher student engagement. Thus, ScratchThAI contributes as a possible and practical solution to CT skill development and CT education improvement under the aforementioned challenges in Thailand.

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Section: Research Design Overviewmentioning

confidence: 99%

ScratchThAI: A conversation-based learning support framework for computational thinking development

2022

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“…Other studies have analyzed periodic snapshots of code in progress. This research has found metrics to drive automated feedback systems [15], detect procrastination in time for early intervention [12], and predict final exam scores [1]. This work demonstrates the promise of keystroke logs for research on program writing.…”

Section: Related Workmentioning

confidence: 86%

Towards an Understanding of Program Writing as a Cognitive Process: Analysis of Keystroke Logs

Davidson

2021

Proceedings of the 17th ACM Conference on International Computing Education Research

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Program writing is difficult to teach, learn, and assess. One challenge is a lack of theory or understanding about what program writing is. My dissertation will address this challenge by applying theories from natural language (NL) writing to try and understand program writing as a cognitive process. By analyzing keystroke logs collected during program writing, I plan to identify similarities with NL writing, potential diagnostic information, and how program writing changes as students become more proficient.

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“…These questions include multiple-choice [18], fill-in-the-blank [19], or those with a solution presented in a structured language, i.e., a mathematical formula [20] or a program [21,22]. The main positive effects of automatic feedback include the students using the feedback for improvement [23], increased student engagement [24,25], and reduction of instructor bias [26]. Despite its benefits, automatic scoring is only one of the potential uses for machine learning and can be expanded to encompass others, including performance prediction, material curation, and course adaptability [27].…”

Section: Related Workmentioning

confidence: 99%

“…Most of the papers addressed works that have effects across disciplines, e.g., using student data to predict performance [26]. For the discipline-specific ones and using the International Standard Classification of Education (ISCED) [33], most of the work fell into the categories of the sciences [37], including areas like geology [38], mathematics [20], computer science [24], computer networking [39] (47% of papers). This is followed by cross-disciplinary applications (32% of the papers), followed by art and humanities (21% papers).…”

Section: Field Of Educationmentioning

confidence: 99%

A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings

Hahn

Navarro

de-la-Fuente-Valentín

et al. 2021

IEEE Access

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Automatic scoring and feedback tools have become critical components of online learning proliferation. These tools range from multiple-choice questions to grading essays using machine learning (ML). Learning environments such as massive open online courses (MOOCs) would not be possible without them. The usage of this mechanism has brought many exciting areas of study, from the design of questions to the ML grading tools' precision and accuracy. This paper analyzes the findings of 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool. This analysis gives an overview of the trends, challenges, and open questions in this research area. The results indicate that automatic scoring and feedback have many advantages. The most important benefits include enabling scaling the number of students without adding a proportional number of instructors, improving the student experience by reducing the time between submission grading and feedback, and removing bias in scoring.On the other hand, these technologies have some drawbacks. The main problem is creating a disincentive to develop innovative answers that do not match the expected one or have not been considered when preparing the problem. Another drawback is potentially training the student to answer the question instead of learning the concepts. With this, given the exitance of a correct answer, such an answer could be leaked to the internet, making it easier for students to avoid solving the problem. Overall, each of these drawbacks presents an opportunity to look at ways to improve technologies to use these tools to provide a better learning experience to students.

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Adaptive Immediate Feedback Can Improve Novice Programming Engagement and Intention to Persist in Computer Science

Cited by 69 publications

References 42 publications

ScratchThAI: A conversation-based learning support framework for computational thinking development

ScratchThAI: A conversation-based learning support framework for computational thinking development

Towards an Understanding of Program Writing as a Cognitive Process: Analysis of Keystroke Logs

A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings

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