Conversation-based assessment: A novel approach to boosting test-taking effort in digital formative assessment

Autor: Seyma N. Yildirim-Erbasli, Okan Bulut
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Computers and Education: Artificial Intelligence, Vol 4, Iss , Pp 100135- (2023)
Druh dokumentu: article
ISSN: 2666-920X
DOI: 10.1016/j.caeai.2023.100135
Popis: Conversational agents such as chatbots have recently gained a lot of traction in various industries, including education, marketing, and healthcare. In K–12 and higher education, conversational agents have been used to boost student learning and motivation. Recently, new applications of conversational agents, such as conversation-based assessments, have emerged in the context of educational assessments. Such applications aim to enhance students' assessment experiences through the use of conversational agents in administering items (or tasks) and providing feedback to students. To discuss the utility of conversational agents in assessments, this paper puts a special emphasis on test-taking effort in low-stakes assessments. It provides a brief introduction to educational assessments and student test-taking motivation, presents the concept of conversational agents for learning and assessment along with several examples, examines the benefits and challenges of conversational agents used for learning and assessment purposes, and discusses the concept of conversation-based assessment. This position paper contributes to the literature by discussing the utility of conversation-based assessments as a novel tool to enhance test-taking effort among students and thereby obtain a more accurate representation of students’ academic performance. Specifically, we describe the advantages and disadvantages of using conversation-based assessments for addressing the lack of test-taking effort in digital formative assessments and discuss how conversation-based assessments can foster a more engaging testing experience for students.
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