Monitoring the Use of Learning Strategies in a Web-Based Pre-course in Mathematics

Derr, Katja; Hübl, Reinhold; Ahmed, Mohammed

doi:10.1007/978-3-319-64792-0_8

Cited by 4 publications

(3 citation statements)

References 60 publications

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“…This review also highlighted the influence of prior academic performance in STEM subjects in dropout prediction. Generally, secondary school graduates are not equally ready for STEM courses due to differences in school types and maths curricula [85]. Students' prior knowledge has been found repeatedly in the selected articles to be related to academic success in STEM subjects [55,56].…”

Section: Student Prior Academic Performancementioning

confidence: 99%

Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review

Herbert

Yeom

et al. 2022

Education Sciences

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Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success and retention. However, the retention factors in learning analytics practice for STEM education have not been fully reviewed and revealed. The purpose of this systematic review is to contribute to this research gap by reviewing the empirical evidence on factors affecting student persistence and retention in STEM disciplines in higher education and how these factors are measured and quantified in learning analytics practice. By analysing 59 key publications, seven factors and associated features contributing to STEM retention using learning analytics were comprehensively categorised and discussed. This study will guide future research to critically evaluate the influence of each factor and evaluate relationships among factors and the feature selection process to enrich STEM retention studies using learning analytics.

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Section: Student Prior Academic Performancementioning

confidence: 99%

Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review

Herbert

Yeom

et al. 2022

Education Sciences

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“…The artificial intelligence tutoring system (AITS) provide tutors on the student's knowledge level and performance while offering recommendations, advice, and actions for students to improve their learning [12]. In another study, feedback in the form of hints and learning recommendation were provided to students after they had taken their diagnostic test [13]. Feedback on the analytics results and recommendations are provided to various stakeholders so that the users' knowledge, behaviour, and experience can be modelled, profiles of the users can be created, the users' knowledge domain can be determined, the usage trend can be identified, and users can personalize and adapt the learning environment to their needs and requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Ibrahim et al [20] "attention, relevance, confidence, and satisfaction (ARSC)" model is used to assess the students' motivation. The large amount of assessment data generated from computer-based and mobile-based assessments can be utilized to track and optimize the learning process, facilitating the improvement and evaluation of conceptual understanding [13], [19]. However, there is a lack of research on the use of learning analytics in mobile learning and assessments that utilize those data to identify the "knowledge vs confidence" quadrant of the learners.…”

Section: Introductionmentioning

confidence: 99%

A review on learning analytics in mobile learning and assessment

Sun,

Lakulu,

Mohd Noor

2024

IJEECS

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Employers are facing difficulties in selecting the most suitable candidates for employment and the transition from education to work is challenging for young graduates. Therefore, it is important to have indicators that could show the suitability of a potential candidate for his/her chosen job. A person who possesses knowledge but lacks confidence may struggle to perform assigned tasks, while an overly confident person with limited knowledge is likely to make errors in their job. Although there is existing research on learning analytics related to assessments, the research on learning analytics specifically focused on the confidence-knowledge relationship based on assessment data is still lacking. This article aims to examine the application of analytics in providing insights based on assessment data that can be utilized by potential employers. To achieve this, a systematic review was carried out, analyzing a total of 141 articles. The findings contribute to a better understanding of the use of assessment analytics in identifying the knowledge-confidence quadrants of students.

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Learners’ Strategies in Interactive Sorting Tasks

Noster

Hershkovitz

Tabach

et al. 2022

Lecture Notes in Computer Science

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Using examples and non-examples is a common technique to demonstrate concepts’ characteristics and boundaries. Based on their properties, certain objects are accepted as examples or non-examples intuitively, while others are accepted or neglected non-intuitively. This 2*2 classification is powerful when designing technology-enhanced learning experiences in which feedback could be provided in real-time. That is, feedback could be based not only on the correctness of student response, but also on the specifics of the objects with which they were engaged. Following this framework, we developed an interactive sorting task that aims at strengthening elementary school students’ understanding of reflective symmetry. We studied learners’ interaction with the objects presented to them, and their success. Our study included 29 elementary school students (ages 9 to 12) from both Israel and Germany. We used screen recording to code participants’ shape-movements, and defined quantitative measures of these movements. Our findings support the need for designing feedback that takes into consideration object’s properties and students’ behavior.

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Monitoring the Use of Learning Strategies in a Web-Based Pre-course in Mathematics

Cited by 4 publications

References 60 publications

Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review

Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review

A review on learning analytics in mobile learning and assessment

Learners’ Strategies in Interactive Sorting Tasks

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