Data science for analyzing and improving educational processes

Aljawarneh, Shadi; Lara, Juan A.

doi:10.1007/s12528-021-09299-7

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…This also implies the use of data mining techniques based on machine learning algorithms and, sometimes, can lead to the design of AI tools. According to Aljawarneh and Lara (2021), the deployment of these techniques "may result of great interests for involved stakeholders (students, instructors, institutions, …) since the extracted knowledge from educational data would be useful to deal with educational problems such as students' performance improvement, high churning rates in educational institutions, learning delays, and so on". In their review paper, they refer to several original contributions of studies where data science techniques have been applied to extract knowledge of interest for educational stakeholders.…”

Section: Artificial Intelligence For Representation Of Learningmentioning

confidence: 99%

Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms

Zanellati

Macauda

Panciroli

et al. 2023

Research on Education and Media

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Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students’ low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification algorithm. In designing this tool, we aim to overcome the use of economic, social, and cultural context indices as main factors for the prediction of a learning gap occurrence. Indeed, we want to include a suitable representation of students’ learning in the model, by exploiting the data collected through the INVALSI tests. We resort to a knowledge-based approach to address this issue and specifically, we try to understand what knowledge is introduced into the model through the representation of learning. In this sense, our proposal allows a students’ learning encoding, which is transferable to different students’ cohort. Furthermore, the encoding methods may be applied to other large-scale assessments test. Hence, we aim to contribute to a debate on knowledge representation in AI tool for education.

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Section: Artificial Intelligence For Representation Of Learningmentioning

confidence: 99%

Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms

Zanellati

Macauda

Panciroli

et al. 2023

Research on Education and Media

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“…"Big" data sets, which often contain thousands or even millions of observations, provide researchers with an opportunity to work with very large samples or even entire populations, which produces more robust results and profound insights about human behavior, reactions, attitudes, opinions, sentiments, etc. The main limitations are that traditional experimental procedures cannot be applied to "big data" and "big data" is not always as reliable as experimental data (for reviews of current applications and limitations of "big data" and data science in education, see Baig et al 2020;Aljawarneh and Lara 2021). To the best of our knowledge, we are the only university in Italy applying data science to language curriculum monitoring.…”

Section: The Learning Contextmentioning

confidence: 99%

Emergency Remote Teaching Student Responses to Intensive versus Extensive Course Modalities during the Pandemic

Ennis

Unterkofler²,

Bonetto³

2023

Lingue Culture Mediazioni - Languages Cultures Mediation (LCM Journal)

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Covid-19 forced university language programs to reevaluate and revolutionize their teaching practices. While in the years preceding the pandemic, many administrators and practitioners were reluctant to embrace blended or fully online language courses, these teaching/learning modalities quickly became the “new normal”. To monitor the efficacy of the courses offered remotely during the 2020/21 academic year, the Language Center of Libera Università di Bolzano administered a series of surveys to students – in addition to the data we routinely collect by way of our “big data” approach to language curriculum monitoring. These courses were offered both in an “extensive” and an “intensive” format. The responses to the surveys – in combination with course enrollment, participation, and completion data – offer an opportunity to investigate students’ attitudes and behavior toward remote teaching and learning, but also to compare intensive language teaching and learning with extensive language teaching and learning, a topic which has not received sufficient attention in the literature to-date. Notwithstanding the well-documented challenges associated with “emergency remote teaching”, we observed many positive experiences and outcomes. Our data is being used to inform decisions regarding which courses will continue to be taught fully or partially online in the years to come.

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“…Over the last decade, as a result of the increasing use of educational technology involving significant amounts of data and learning analytics, personalized learning has gained increasing popularity. This has led a number of research groups to study the adaptation of personalized learning into educational technologies, leveraging insights from learning analytics [5,19]. Adaptive Learning Systems (ALSs) [18] achieve personalized learning experiences by using users' prior interactions and by adjusting the learning content to match individual preferences and requirements.…”

Section: Introductionmentioning

confidence: 99%

Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students' Training Data

Kandemir,

Vie,

Sanchez-Ayte

et al. 2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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Accurate estimation of question difficulty and prediction of student performance play key roles in optimizing educational instruction and enhancing learning outcomes within digital learning platforms. The Elo rating system is widely recognized for its proficiency in predicting student performance by estimating both question difficulty and student ability while providing computational efficiency and real-time adaptivity. This paper presents an adaptation of a multi-concept variant of the Elo rating system to the data collected by a medical training platform-a platform characterized by a vast knowledge corpus, substantial inter-concept overlap, a huge question bank with significant sparsity in user-question interactions, and a highly diverse user population, presenting unique challenges. Our study is driven by two primary objectives: firstly, to comprehensively evaluate the Elo rating system's capabilities on this real-life data, and secondly, to tackle the issue of imprecise earlystage estimations when implementing the Elo rating system for online assessments. Our findings suggest that the Elo rating system exhibits comparable accuracy to the well-established logistic regression model in predicting final exam outcomes for users within our digital platform. Furthermore, results underscore that initializing Elo rating estimates with historical data remarkably reduces errors and enhances prediction accuracy, especially during the initial phases of student interactions.

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Data science for analyzing and improving educational processes

Cited by 15 publications

References 5 publications

Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms

Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms

Emergency Remote Teaching Student Responses to Intensive versus Extensive Course Modalities during the Pandemic

Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students' Training Data

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