The influence of social data on student success prediction

Bydźovská, Hana; Popelínský, Lubomír

doi:10.1145/2628194.2628199

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…For example, Lacave et al (2018) use enrolment age, prior choice of subject and information on scholarships in order to predict student dropouts. In addition to demographic variables (Aulck et al 2017;Sarker 2014), the student's academic self-concept, academic history and workrelated data are used to predict student performance (Mitra and Goldstein 2015), while others use GPA, academic load, and access to counselling (Rogers et al 2014), the student's financial background (Thammasiri et al 2014), or academic performance history (Bydzovska and Popelinsky 2014;Sales et al 2016;Srilekshmi et al 2016) as predictors of students at risk.…”

Section: Predictors For Study Successmentioning

confidence: 99%

Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

200

109

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Study success includes the successful completion of a first degree in higher education to the largest extent, and the successful completion of individual learning tasks to the smallest extent. Factors affecting study success range from individual dispositions (e.g., motivation, prior academic performance) to characteristics of the educational environment (e.g., attendance, active learning, social embeddedness). Recent developments in learning analytics, which are a socio-technical data mining and analytic practice in educational contexts, show promise in enhancing study success in higher education, through the collection and analysis of data from learners, learning processes, and learning environments in order to provide meaningful feedback and scaffolds when needed. This research reports a systematic review focusing on empirical evidence, demonstrating how learning analytics have been successful in facilitating study success in continuation and completion of students' university courses. Using standardised steps of conducting a systematic review, an initial set of 6220 articles was identified. The final sample includes 46 key publications. The findings obtained in this systematic review suggest that there are a considerable number of learning analytics approaches which utilise effective techniques in supporting study success and students at risk of dropping out. However, rigorous, large-scale evidence of the effectiveness of learning analytics in supporting study success is still lacking. The tested variables, algorithms, and methods collected in this systematic review can be used as a guide in helping researchers and educators to further improve the design and implementation of learning analytics systems.

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Section: Predictors For Study Successmentioning

confidence: 99%

Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

200

109

View full text Add to dashboard Cite

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“…Then we calculated the success rate for the data from the year 2013. The results were compared to the baseline (a prediction into the majority class) and also to the results on the same data set obtained by our previous approach [1,2] using classification algorithms (CA) on study-related and social behavior data about students. We computed the accuracy and also MAE because there was a significant difference when we predicted the grade 1 or 2.5 and the student obtained 3.…”

Section: Resultsmentioning

confidence: 99%

“…We compare the results with the results of our previous approach when we utilized classification algorithms implemented in Weka [7] on study-related and social behavior data about students [1,2]. The reliable prediction might help teachers to identify weak students in order to help them to pass the course or to achieve better grades.…”

Section: Introductionmentioning

confidence: 99%

Student Performance Prediction Using Collaborative Filtering Methods

Bydźovská

2015

Lecture Notes in Computer Science

Self Cite

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Abstract. This paper shows how to utilize collaborative filtering methods for student performance prediction. These methods are often used in recommender systems. The basic idea of such systems is to utilize the similarity of users based on their ratings of the items in the system. We have decided to employ these techniques in the educational environment to predict student performance. We calculate the similarity of students utilizing their study results, represented by the grades of their previously passed courses. As a real-world example we show results of the performance prediction of students who attended courses at Masaryk University. We describe the data, processing phase, evaluation, and finally the results proving the success of this approach. Keywords: Student performance · Prediction · Collaborative filtering methods · Recommender system IntroductionStudent modeling is a crucial task in educational environment. We utilize collaborative filtering (CF) methods [5] to predict students' performance. CF is a common technique for generating personalized recommendation. The main idea of collaborative recommendation approaches is to exploit information about the past behavior of users for predicting the behavior of the current user of the system. We are interested in predicting whether students will succeed or fail in a course that they want to enroll in at the beginning of a term. This means that we have no information about students' knowledge, skills or enthusiasm for a particular course. In this paper, we report on a possibility to estimate student performance in a particular course based only on knowledge of student's previously passed courses.We utilize different CF methods to estimate the final prediction. We compare the results with the results of our previous approach when we utilized classification algorithms implemented in Weka [7] on study-related and social behavior data about students [1,2]. The reliable prediction might help teachers to identify weak students in order to help them to pass the course or to achieve better grades. We can also recommend passable voluntary courses to the students and warn them about difficult mandatory courses that they have to pass.

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“…1. Bydzovska and Popelinsky [41] applied social network analysis utilising student datasets including study-related, social behaviour and data concerning previously passed courses (key indicators being the two aforementioned variables). 2.…”

Section: Indicators For Predicting Students' Social/learning Behaviour Including Engagementmentioning

confidence: 99%

Reflections on Different Learning Analytics Indicators for Supporting Study Success

Yau

2020

iJAI

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<p class="0abstract"><span lang="EN-AU">Common factors, which are related to study success include students’ sociodemographic factors, cognitive capacity, or prior academic performance, and individual attributes as well as course related factors such as active learning and attention or environmental factors related to supportive academic and social embeddedness. In addition, there are various stages of a learner’s learning journey from the beginning when commencing learning until its completion, as well as different indicators or variables that can be examined to gauge or predict how successfully that journey can or will be at different points during that journey, or how successful learners may complete the study and thereby acquiring the intended learning outcomes. The aim of this research is to gain a deeper understanding of not only if learning analytics can support study success, but which aspects of a learner’s learning journey can benefit from the utilisation of learning analytics. We, therefore, examined different learning analytics indicators to show which aspect of the learning journey they were successfully supporting. Key indicators may include GPA, learning history, and clickstream data. Depending on the type of higher education institution, and the mode of education (face-to-face and/or distance), the chosen indicators may be different due to them having different importance in predicting the learning outcomes and study success.</span></p>

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The influence of social data on student success prediction

Cited by 8 publications

References 3 publications

Utilising learning analytics to support study success in higher education: a systematic review

Utilising learning analytics to support study success in higher education: a systematic review

Student Performance Prediction Using Collaborative Filtering Methods

Reflections on Different Learning Analytics Indicators for Supporting Study Success

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