Literaturnachweis - Detailanzeige
| Autor/inn/en | Marwan, Samiha; Shi, Yang; Menezes, Ian; Chi, Min; Barnes, Tiffany; Price, Thomas W. |
|---|---|
| Titel | Just a Few Expert Constraints Can Help: Humanizing Data-Driven Subgoal Detection for Novice Programming [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021). |
| Quelle | (2021), (13 Seiten)
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Expertise; Models; Feedback (Response); Identification; Programming; Problem Solving; Accuracy; Scores; Data; Formative Evaluation; Data Collection; Data Analysis |
| Abstract | Feedback on how students progress through completing subgoals can improve students' learning and motivation in programming. Detecting subgoal completion is a challenging task, and most learning environments do so either with "expert-authored" models or with "data-driven" models. Both models have advantages that are complementary -- expert models encode domain knowledge and achieve reliable detection but require "extensive authoring efforts" and often cannot capture all students' possible solution strategies, while data-driven models can be easily scaled but may be less accurate and interpretable. In this paper, we take a step towards achieving the best of both worlds -- utilizing a data-driven model that can intelligently detect subgoals in students' correct solutions, while benefiting from human expertise in editing these data-driven subgoal rules to provide more accurate feedback to students. We compared our hybrid "humanized" subgoal detectors, built from data-driven subgoals modified with expert input, against an existing data-driven approach and baseline supervised learning models. Our results showed that the hybrid model outperformed all other models in terms of overall accuracy and F1-score. Our work advances the challenging task of automated subgoal detection during programming, while laying the groundwork for future hybrid expert-authored/data-driven systems. [For the full proceedings, see ED615472.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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