A Procedural Learning and Institutional Analytics Framework

Amigud, Alexander; Daradoumis, Τhanasis; Arnedo-Moreno, Joan; Rodríguez, M. Elena

doi:10.1007/978-3-319-68318-8_2

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Learning analytics provide teachers with insights they can use to develop a better quality of content, improve student learning activities, and provide real-time personalised feedback to students (Amigud et al, 2018;Jivet & Drachsler, 2022). Results of learning analytics are presented to instructors and students in the form of visualisation dashboards.…”

Section: Learning Analyticsmentioning

confidence: 99%

Data-Driven Approach to Teaching Research Methods: iMethod, a Proof-of-Concept

Daniel

2023

ecrm

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Courses in research methods play a critical role in postgraduate education. However, postgraduate students face considerable challenges in learning the subject matter. The present paper introduces iMethod, a software application designed to track students' engagement with the content of research methods courses. iMethod harvested data, analysed students' engagement, and presented the outcome to a teacher through a dashboard rendered in real-time. As a proof-of-concept, a usability study on iMethod was conducted with a sample of postgraduate students and early career academics (n=37) at a research-intensive university in New Zealand. Key findings suggest that students and academic staff found iMethod a valuable application for enhancing research methods learning. In particular, students value its content for understanding fundamental concepts while valuing its inquiry facilitation and providing helpful information on research methods. Participants said iMethod could expand knowledge, offers guidance, and the content is accessible to a diverse range of students. Lastly, participants reported that iMethod facilitated easy knowledge sharing, and the interface design was user-friendly and intuitive. The paper contributes to the growing need to promote research into the curriculum design of research methodology programmes and the quality of teaching research methods courses.

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Section: Learning Analyticsmentioning

confidence: 99%

Data-Driven Approach to Teaching Research Methods: iMethod, a Proof-of-Concept

Daniel

2023

ecrm

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“…Machine learning has become crucial for solving complex problems and has been successfully applied in a wide range of areas, including the stock market, games, medical diagnostics, robotics, information security, and education [25]. In education, it has been used for a variety of purposes, such as predicting student dropout and retention [26], admission decisions and course scheduling [27], profiling students and modeling their learning behavior [28], and evaluating student engagement and academic integrity [29].…”

Section: B Machine Learningmentioning

confidence: 99%

Arabic Questions Classification Using Modified TF-IDF

Alammary

2021

IEEE Access

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Classifying the cognitive levels of assessment questions according to Bloom's taxonomy can help instructors design effective assessments that are well aligned with the intended learning outcomes. However, the classification process is time consuming and requires experience. Many studies have attempted to automate the process by utilizing different machine learning and text mining approaches, but none has examined the classification of Arabic questions. The purpose of this study is to examine this research gap and to introduce a new feature extraction method that would better suit Arabic questions and their unique characteristics. It also aims to provide Arab instructors with a tool that can help them automatically classify their assessment questions. To accomplish this purpose, the study developed a dataset of more than 600 Arabic assessment questions. It then proposed a modified term frequency-inverse document frequency (TF-IDF) method for extracting features from Arabic questions. Unlike the traditional TF-IDF, the proposed method was designed to take the nature of assessment questions into consideration. It was evaluated by comparing it to two methods that have been used for classifying English questions, i.e., the traditional TF-IDF and a modified TF-IDF method called term frequency part-of-speech-inverse document frequency (TFPOS-IDF). A t-test was utilized to examine whether the difference in performance between the three methods was statistically significant. The proposed method outperformed the two other methods. The overall accuracy, precision, and recall scored by the proposed method were significantly higher than those scored by the traditional TF-IDF and TFPOS-IDF methods. The evaluation results indicate the promising potential of the proposed method, which can be extended to other languages.

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A Procedural Learning and Institutional Analytics Framework

Cited by 2 publications

References 20 publications

Data-Driven Approach to Teaching Research Methods: iMethod, a Proof-of-Concept

Data-Driven Approach to Teaching Research Methods: iMethod, a Proof-of-Concept

Arabic Questions Classification Using Modified TF-IDF

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