Spatial computing goes to education and beyond

Heo, Joon; Yoon, Sanghyun; Oh, Won Seob; Won, Jong; Ju, Sungha; Yun, Sung Bum

doi:10.1145/3006386.3006389

Cited by 7 publications

References 6 publications

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Spatial perspectives on student profiling to inform open distance e-learning (ODeL) in various geographical contexts: a case study from the Global South

2023

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Sustainable Development Goal (SDG) 4 underpins the other SDGs and emphasizes the importance of increasing access to inclusive, equitable, quality education. Equitable and inclusive access to education remains a pressing issue in many parts of the world, especially in the Global South. Developments and challenges experienced in the Global South have increased awareness of the potential of open distance e-learning to increase access to higher education. This potential has to be considered from the perspective of the digital divide. Analyzing student data can play an important role in obtaining insights into students' lives to improve the quality of the educational services provided.This paper aims to reflect on the use of spatial statistics in compiling a profile of the students registered for a Geography module at the University of South Africa. This paper has two objectives. The first is to specify the broad student profile in terms of the geographical location of students. The second objective is to reflect on the lifeworlds of the students in the different clusters and the challenges they may face in successfully completing their studies. Four representative student profiles could be compiled using lifestyle data. Marked differences in lifestyle factors between the four identified profile types were observed. These may directly impact students' ability to progress with their studies.

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Spatial perspectives on student profiling to inform open distance e-learning (ODeL) in various geographical contexts: a case study from the Global South

2023

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Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

et al. 2021

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Digital data trails from disparate sources covering different aspects of student life are stored daily in most modern university campuses. However, it remains challenging to (i) combine these data to obtain a holistic view of a student, (ii) use these data to accurately predict academic performance, and (iii) use such predictions to promote positive student engagement with the university. To initially alleviate this problem, in this paper, a model named Augmented Education (AugmentED) is proposed. In our study, (1) first, an experiment is conducted based on a real-world campus dataset of college students (N = 156) that aggregates multisource behavioral data covering not only online and offline learning but also behaviors inside and outside of the classroom. Specifically, to gain in-depth insight into the features leading to excellent or poor performance, metrics measuring the linear and nonlinear behavioral changes (e.g., regularity and stability) of campus lifestyles are estimated; furthermore, features representing dynamic changes in temporal lifestyle patterns are extracted by the means of long short-term memory (LSTM). (2) Second, machine learning-based classification algorithms are developed to predict academic performance. (3) Finally, visualized feedback enabling students (especially at-risk students) to potentially optimize their interactions with the university and achieve a study-life balance is designed. The experiments show that the AugmentED model can predict students' academic performance with high accuracy. INDEX TERMS academic performance prediction, behavioral pattern, digital campus, machine learning (ML), long short-term memory (LSTM)

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