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This study investigates the application of machine learning and BERT models to identify topic categories in helpful online course reviews and uncover factors that influence the overall satisfaction of learners in massive open online courses (MOOCs). The research has three main objectives: (1) to assess the effectiveness of machine learning models in classifying review helpfulness, (2) to evaluate the performance of fine-tuned BERT models in identifying review topics, and (3) to explore the factors that influence learner satisfaction across various disciplines. The study uses a MOOC corpus containing 102,184 course reviews from 401 courses across 13 disciplines. The methodology involves...

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The use of standardized test formats in the assessment of historical competencies has recently come under severe criticism, especially in the United States, where standardized tests are particularly common. History researchers have argued that open-ended items are more appropriate for assessment. However, providing largescale evaluations of open-ended answers is time consuming and poses challenges regarding the objectivity, validity, and replicability of ratings. To address this issue, we investigated the extent to which computer-based evaluation methods are suitable for evaluating student answers by combining qualitative methods from history education research with quantitative, computer-...

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The application of learning sciences with technology has been shown to boost learner interactions, yet the potential of advanced tool, particularly those that leverage Natural Language Processing (NLP), still very much untapped in learning contexts. This paper speaks to this age-old problem of generating quality Multiple-Choice questions (MCQs) – a prevalent but time-consuming mode of assessment – via the suggested comprehensive comparison study of template-based AI solutions. The study contrasts general-purpose Large Language Models (LLMs) with specialized MCQ-focused AI programs. The scientific approach employed was quite stringent, where each of the software applications was benchmarked...

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English grammar multiple-choice questions (MCQs) can be automatically generated to reduce preparation time. Previous studies have focused on semiautomated methods based on the transformation of human-made sentences/articles into MCQs, owing to which the number of generated questions is dependent on the size of a given text corpus. This study proposes an artificial intelligence-assisted MCQ generation system that increases the number of generable questions using controllable text generation techniques. In this system, the questions for MCQs are generated using a text generation model trained using the Text-to-Text Transfer Transformer (T5) architecture, a powerful deep learning model for...

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Even before the age of artificial intelligence, automated scoring received considerable attention in educational measurement. However, its application to constructed response (CR) items in international large-scale assessments (ILSAs) has remained a challenge, primarily due to the difficulty of handling multilingual responses spanning many languages. This study addresses this challenge by investigating two machine learning approaches — supervised and unsupervised learning — for scoring multilingual responses. We explored various scoring methods to assess three science CR items from TIMSS 2023 across all participating countries and 42 languages. The results showed that the supervised...

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Feedback is a crucial element of a student's learning process. It enables students to identify weaknesses and improve self-regulation. However, studies show this to be an area of great dissatisfaction in higher education. With ever-growing course participation numbers, delivering effective feedback is becoming an increasingly challenging task. The efficacy of feedback will depend on four levels of feedback; namely, feedback about the self, task, process or self-regulation. Hence, this paper explores the use of automated content analysis to examine feedback provided by instructors for feedback practices measured on self, task, process, and self-regulation levels. For this purpose, four...

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This study presents an innovative solution for evaluating university-level teaching quality using artificial intelligence (AI), focusing on key aspects such as clarity of explanation and lecture structure. Traditional student surveys, while valuable, are often subject to biases and lack the necessary granularity, creating a need for objective, scalable solutions that provide consistent results. We propose an automated framework utilizing advanced natural language processing (NLP) models to assess teaching quality based on lecture transcripts. The methodology combines AI-driven transcription, machine learning-based assessments, and correlation with institutional student evaluations to...

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In this work, we study the feasibility of using machine learning and natural language processing methods for assessing writing proficiency in Danish with respect to text construction, sentence construction, and use of modifiers. Our work is based on the analytical framework for scoring early writing proposed by Kabel et al. (2022), where each text is first annotated by a human expert according to a predefined coding scheme and subsequently scored using statistical Rasch modeling (Rasch, 1960). We investigate two different strategies for estimating these scores automatically: 1) we propose a system for identifying the central linguistic features automatically mimicking the role of the human...

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Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online delivery. There is an opportunity to compare the experiences of students in the MOOC environment in this period, particularly in terms of the difference of engagement, semantics and sentiment/stress behaviours in 2019 and 2020. In this study, we use a dataset from AdelaideX between this time period to identify the most significant features...

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Student feedback on teaching at the end of the semester is an important source of information for instructors to gain insights into the effectiveness of their teaching. There are usually two forms of student feedback: quantitative scores and qualitative feedback. Quantitative scores can usually be easily summarized, while the analysis of qualitative feedback is usually effort-intensive as it deals with text. To help instructors glean insights from students' qualitative feedback, many previous studies used unsupervised approaches (i.e., topic modelling) for topic extraction in student feedback. Although topic modelling enables automated detection of previously unseen topics with minimal...

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