This article situates the potential for intellectual work to be renewed through an enriched engagement with the relationship between indigenous protocols and artificial intelligence (AI). It situates this through a dialectical storytelling of the contradictions that emerge from the relationships between humans and capitalist technologies, played…

Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students' reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed…

Think-aloud protocols are a common method to study self-regulated learning (SRL) during learning by problem-solving. Previous studies have manually transcribed and coded students' verbalizations, labeling the presence or absence of SRL strategies and then examined these SRL codes in relation to learning. However, the coding process is difficult to…

Contemporary research that looks at self-regulated learning (SRL) as processes of learning events derived from trace data has attracted increasing interest over the past decade. However, limited research has been conducted that looks into the validity of trace-based measurement protocols. In order to fill this gap in the literature, we propose a…

In the field of education, the think-aloud protocol is commonly used to encourage learners to articulate their thoughts during the learning process, providing observers with valuable insights into learners' cognitive processes beyond the final learning outcomes. However, the implementation of think-aloud protocols faces challenges such as task…

Think-aloud protocols are a standard method to study self-regulated learning (SRL) during learning by problem-solving. Advances in automated transcription and large language models (LLMs) have automated the transcription and labeling of SRL in these protocols, reducing manual effort. However, while effective in many emerging applications, previous…

Artificial intelligence (AI) technologies have the potential to reduce the workload for the second language (L2) teachers and test developers. We propose two AI distractor-generating methods for creating Chinese vocabulary items: semantic similarity and visual similarity. Semantic similarity refers to antonyms and synonyms, while visual similarity…