Innovative Learning Analytics Research at a data-driven HEI

Azcona, David; Corrigan, Owen I.; Scanlon, Philip; Smeaton, Alan F.

doi:10.4995/head17.2017.5245

Cited by 9 publications

(5 citation statements)

References 2 publications

(2 reference statements)

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“…The computer programming grading system has been used for several years on a variety of programming courses at our University. This allowed researchers and Faculty to gather a fine-grained digital footprint of students learning programming at our University [5]. Recently, research in Learning Analytics has focused on Predictive Modelling and identifying those students having difficulties with course material, also in programming courses [9], and offering remediation, personalized feedback and interventions to students using Machine Learning techniques [6,8].…”

Section: Datamentioning

confidence: 99%

user2code2vec

Azcona

Arora

Hsiao

et al. 2019

Proceedings of the 9th International Conference on Learning Analytics &Amp; Knowledge

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In this work, we propose a new methodology to profile individual students of computer science based on their programming design using a technique called embeddings. We investigate different approaches to analyze user source code submissions in the Python language. We compare the performances of different source code vectorization techniques to predict the correctness of a code submission. In addition, we propose a new mechanism to represent students based on their code submissions for a given set of laboratory tasks on a particular course. This way, we can make deeper recommendations for programming solutions and pathways to support student learning and progression in computer programming modules effectively at a Higher Education Institution. Recent work using Deep Learning tends to work better when more and more data is provided. However, in Learning Analytics, the number of students in a course is an unavoidable limit. Thus we cannot simply generate more data as is done in other domains such as FinTech or Social Network Analysis. Our findings indicate there is a need to learn and develop better mechanisms to extract and learn effective data features from students so as to analyze the students' progression and performance effectively.

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Section: Datamentioning

confidence: 99%

user2code2vec

Azcona

Arora

Hsiao

et al. 2019

Proceedings of the 9th International Conference on Learning Analytics &Amp; Knowledge

Self Cite

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“…Although results from previous studies indicate that the use of digital platforms may improve how students engage in help-seeking practices and increases mental health and wellbeing literacy [46][47][48], currently there remains a gap in our understanding around the kind of data that university students create and find meaningful, the specific needs that they have in relation to this, and how they can best be supported in their well-being and personal development throughout HE and beyond [49].…”

Section: Related Workmentioning

confidence: 99%

Facilitated Collaborative Group Co-Design of Hypothetical Digital Tools to Understand University Students’ Well-being Support Needs and Requirements

Bannigan,

McGrath,

Matthews

et al. 2024

SN COMPUT. SCI.

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The number of students in higher education encountering issues with their well-being is increasing every year, with many struggling to seek appropriate support due to reasons such as stigma or lack of access to services. Consequently, digital well-being support tools may be best placed to address their needs; however, few of these tools involve students in the design process, and thus, there is a lack of clarity as to whether these tools meet the needs of students. Therefore, the aim of the present research was to provide a series of design considerations that should be incorporated in the design and development of digital tools to support student well-being. Co-design methods were used to develop these design considerations by understanding the well-being domains in which students want support, their views on existing digital supports, and their desired features in a digital support tool. Undergraduate students enrolled in a well-being module at an Irish University completed a facilitated group assignment in which they were required to co-design a hypothetical digital tool to support other students’ well-being. Deductive thematic analysis of the 10-min presentations of their tools was carried out. The overall findings were that: Students recognize all well-being domains as interconnected and view social connectedness and time management as crucial for them to find good balance in their lives and to thrive; Current digital well-being tools do not appropriately meet their needs, being too expensive, unmotivating, not suited to the student lifestyle and perceived as untrustworthy; Students desire a digital well-being tool that can give them structured and streamlined support and that clearly relates to their specific university environment; Specific desired features include the ability to log and visualize data, personalization capabilities, a motivational rewards system, and trustworthy educational information, tips and advice.

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“…Researchers have also used students' data to identify the factors behind the predicted performance in quizzes and assignments and provide information on how the prediction could be improved by listing activities to be performed (e.g. watching a video) (Azcona et al, 2017).…”

Section: Supporting Learningmentioning

confidence: 99%

“…In addition to the development of dashboards, researchers (Lonn et al, 2015) also endeavoured to discover the relationship between advisors' use of learning analytics early warning system, which used information from a learning management system to provide weekly updates about student engagement and performance to advisors, and their students' performance during a summer bridge program. Also, researchers used on-campus geolocation data to identify study groups and examine the performance among group members (Azcona et al, 2017) or examined the link between learning input (the frequency of playing videos) and performance (Ji & Han, 2019). Similarly, others discussed the associations between performance, submission time and the number of submissions, how work at night or weekends impacts performance, or how different group work patterns affect performance (Apiola et al, 2019).…”

Section: Supporting Learningmentioning

confidence: 99%

A decade of research into the application of big data and analytics in higher education: A systematic review of the literature

Stojanov

Daniel

2023

Educ Inf Technol

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The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and encompassing 75 papers, analysed the applications of Big Data and analytics in higher education. The focus was on their usage in supporting learning, teaching and administration as reported in papers indexed in SCOPUS, Web of Science and IEEE Xplore. The key findings from the review revealed that Big Data and analytics are predominantly used to support learning and, to a lesser extent, guide teaching and informing administrative decision-making processes. The review also identified a set of studies focused on supporting student well-being. Further, we extend the use of Big Data in higher education to include the well-being of students and staff. This paper contributes to the growing debate on the practical use of Big Data and analytics to provide valuable insights for solving systemic challenges facing high education in the twenty-first century.

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Innovative Learning Analytics Research at a data-driven HEI

Abstract: Abstract

Cited by 9 publications

References 2 publications

user2code2vec

user2code2vec

Facilitated Collaborative Group Co-Design of Hypothetical Digital Tools to Understand University Students’ Well-being Support Needs and Requirements

A decade of research into the application of big data and analytics in higher education: A systematic review of the literature

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