Beyond binary correctness: Classification of students’ answers in learning systems

PELÁNEK, Radek and Tomáš EFFENBERGER. Beyond binary correctness: Classification of students’ answers in learning systems. User Modeling and User-Adapted Interaction. Springer, 2020, vol. 30, No 5, p. 867-893. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-020-09265-5.

Basic information

• Original name: Beyond binary correctness: Classification of students’ answers in learning systems
• Authors: PELÁNEK, Radek (203 Czech Republic, guarantor, belonging to the institution) and Tomáš EFFENBERGER (203 Czech Republic, belonging to the institution).
• Edition: User Modeling and User-Adapted Interaction, Springer, 2020, 0924-1868.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Switzerland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 4.412
• RIV identification code: RIV/00216224:14330/20:00116670
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1007/s11257-020-09265-5
• UT WoS: 000530578600001
• Keywords in English: adaptive learning; student modeling; answer classification; response time
• Tags: International impact, Reviewed

Abstract

Adaptive learning systems collect data on student performance and use them to personalize system behavior. Most current personalization techniques focus on the correctness of answers. Although the correctness of answers is the most straightforward source of information about student state, research suggests that additional data are also useful, e.g., response times, hints usage, or specific values of incorrect answers. However, these sources of data are not easy to utilize and are often used in an ad hoc fashion. We propose to use answer classification as an interface between raw data about student performance and algorithms for adaptive behavior. Specifically, we propose a classification of student answers into six categories: three classes of correct answers and three classes of incorrect answers. The proposed classification is broadly applicable and makes the use of additional interaction data much more feasible. We support the proposal by analysis of extensive data from adaptive learning systems.

Links

• MUNI/A/1050/2019, interní kód MU: Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX (Acronym: SV-FI MAV IX)

Investor: Masaryk University, Category A

• MUNI/A/1076/2019, interní kód MU: Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 20 (Acronym: SKOMU)

Investor: Masaryk University, Category A