From belief to evidence: simpler immediate feedback improves language learning and confidence in semi-open-ended questions

Semi-open-ended questions (SOE) are commonly used in online foreign language assignments; however, the effects of online feedback on these question types have received limited research attention. To address this gap, the present study employed an instructor survey and an in-class experiment to investigate how different forms of immediate feedback to SOE questions influence learning outcomes and judgment of learning (JoL). Survey findings revealed that although most instructors had access to platforms enabling immediate feedback, few utilized this feature for open-ended questions. Moreover, a majority of instructors believed that the most effective approach was to provide both correct responses (CR) and elaborated feedback (EF). Contrary to these beliefs, our experimental findings demonstrated that CR alone—feedback that simply provides the correct answer—was more effective than more complex feedback conditions, including a combination of CR and EF. Students who received CR required the least time, achieved the highest learning gains and reported stronger JoL, while CR and EF together did not yield additional benefits despite requiring more time to process. These findings challenge the common assumption that “more feedback is always better” and highlight the efficiency and pedagogical value of CR feedback for SOE questions in online foreign language learning. The study offers practical implications for instructors designing feedback strategies in technology-mediated environments.

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Publication Details

Journal
Frontiers in Education
Published
2025-10-22
DOI
https://doi.org/10.3389/feduc.2025.1654809
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00

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article

From belief to evidence: simpler immediate feedback improves language learning and confidence in semi-open-ended questions

Qingyu Yang, Xiwen Lu, Neil T. Heffernan, Jian Wei
Frontiers in Education
Intelligent Tutoring Systems and Adaptive Learning
article

From belief to evidence: simpler immediate feedback improves language learning and confidence in semi-open-ended questions

Qingyu Yang, Xiwen Lu, Neil T. Heffernan, Jian Wei
article en

Abstract

Semi-open-ended questions (SOE) are commonly used in online foreign language assignments; however, the effects of online feedback on these question types have received limited research attention. To address this gap, the present study employed an instructor survey and an in-class experiment to investigate how different forms of immediate feedback to SOE questions influence learning outcomes and judgment of learning (JoL). Survey findings revealed that although most instructors had access to platforms enabling immediate feedback, few utilized this feature for open-ended questions. Moreover, a majority of instructors believed that the most effective approach was to provide both correct responses (CR) and elaborated feedback (EF). Contrary to these beliefs, our experimental findings demonstrated that CR alone—feedback that simply provides the correct answer—was more effective than more complex feedback conditions, including a combination of CR and EF. Students who received CR required the least time, achieved the highest learning gains and reported stronger JoL, while CR and EF together did not yield additional benefits despite requiring more time to process. These findings challenge the common assumption that “more feedback is always better” and highlight the efficiency and pedagogical value of CR feedback for SOE questions in online foreign language learning. The study offers practical implications for instructors designing feedback strategies in technology-mediated environments.

Frontiers in EducationVol. 10
Worcester Polytechnic Institute (US), University of Rhode Island (US), Brandeis University (US)
National Science Foundation, Worcester Polytechnic Institute, Office of Naval Research
Openalex Percentile: Top 84%
Intelligent Tutoring Systems and Adaptive Learning
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