Support Learning Analytics Research Articles

A Mini Review on Technologies and Storage Platforms in Supporting Learning Analytics

A Mini Review on Technologies and Storage Platforms in Supporting Learning Analytics

Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces:

Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to uncover valuable educational insights from individuals’ social and spatial data traces. These traces are captured automatically through sensing technologies in physical learning spaces, and the research is commonly based on the theoretical foundations of social constructivism and cultural anthropology. With its growing empirical basis, a systematic literature review is timely in order to provide educational researchers and practitioners with a detailed summary of the emerging works and the opportunities enabled by SSLA. This paper presents a systematic review of 25 peer-reviewed articles on SSLA published between 2011 and 2023. Descriptive, network, and thematic analyses were conducted to identify the citation networks, essential components, opportunities, and challenges enabled by SSLA. The findings illustrated that SSLA provides the opportunity to (1) contribute unobtrusive and unsupervised research methodologies, (2) support educators’ classroom orchestration through visualizations, (3) support learner reflection with continuous and reliable evidence, (4) develop novel theories about social and collaborative learning, and (5) empower educational stakeholders with the quantitative data to evaluate different learning spaces. These findings could support learning analytics and educational technology scholars and practitioners to better understand and utilize SSLA to support future educational research and practice.

Supporting Learning Analytics Adoption: Evaluating the Learning Analytics Capability Model in a Real-World Setting

Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.

Web-Based 3D and [formula omitted] VR Materials for IoT Security Education and Test Supporting Learning Analytics

Internet of Things (IoT) technology is developing rapidly and attract more and more attentions in recent years. With the maturity of this technology, many companies have already provided various IoT devices to consumers, such as smart speakers and smart meters. These devices are wildly used not only in researches but also in many people’s daily lives. However, with the widely use of these devices, the security problems of IoT devices become more and more important. Although the device makers will provide the security solutions in technology level, users and IoT professors should have enough security knowledge as well. In this paper, we introduce a novel framework for generating web-based 3D and 360-degree VR materials for IoT security education. Our framework also supports the automatic generation of quizzes of IoT security, which will be used for evaluating the learning effects. The generated e-learning materials and quizzes can collect users’ log data for further analyzation. In this paper,we import two visually analyzing tools, Time Tunnel and Cubic Gantt Chart, into our framework. By using these two tools, we can obtain the activity patterns of the learners in different score ranges. Based on such result, we can improve the e-learning materials for increasing the educational effects, or provide advices to help learners effectively use our e-learning materials.

Kajian Penggunaan Data Log Mahasiswa untuk Berbagai Permasalahan Analisis Pembelajaran

Pada saat ini, penyelenggaraan sistem pembelajaran daring menjadi hal yang penting di tengah pandemi untuk menekan persebaran virus COVID-19. Namun, sistem ini sangat sulit menjaga motivasi dan tingkat keterlibatan mahasiswa karena tidak ada interaksi langsung antara pengajar dengan mahasiswa. Makalah ini meninjau penggunaan data log mahasiswa untuk kebutuhan analisis pembelajaran guna memprediksi kinerja atau kecenderungan drop-out mahasiswa dari suatu mata kuliah dengan melihat pada data log interaksi mahasiswa dengan sistem dan data demografis mahasiswa menggunakan suatu data terbuka, yaitu Open University Learning Analytics Dataset (OULAD). Dari tinjauan beberapa artikel penelitian yang merujuk pada dataset tersebut, ada beberapa hal yang perlu ditinjau: 1) permasalahan yang sering diangkat, yaitu prediksi kecenderungan gagal dari mata kuliah tertentu, prediksi kinerja, dan prediksi keterlibatan mahasiswa; 2) fitur yang digunakan pada saat pemodelan, yaitu fitur demografis dan interaksi, baik yang diringkas secara harian atau mingguan dengan berbagai representasi fitur; 3) metode analisis pembelajaran yang secara khusus menggunakan metode pembelajaran mesin yang sering digunakan, yaitu Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), dan Long Short-Term Memory (LSTM). Makalah ini juga mendiskusikan proses mitigasi dari mahasiswa yang berisiko, perancangan sistem data yang mendukung analisis pembelajaran, dan permasalahan yang sering ditemui pada saat proses pemodelan.

Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment

Technology enhanced learning (TEL) such as online learning environment with adaptive technologies has gained growing interest in recent past in the field of teaching and learning. In this context, mobile learning has got much momentum and is exemplified by diverse characteristics associated with the technologies and devices used, the enormous size of data generated throughout a learning session, and the interactions among the learners that occur outside the classroom. Consequently, sophisticated data analysis techniques are required to handle the intricacy of mobile learning and analyze the vast amount of datasets to enhance the learning experiences of mobile learners. This has led to the adoption of big data analytics for efficient processing of big learning data to add value to the mobile learning environments. Yet limited processing capability of the mobile devices is another key challenge faced by such big data analytics in mobile learning environments. To overcome this limitation, certain heavy computational parts could be offloaded to the cloud which can provide enough computation and storage resources. To this end, this paper presents a cloud based mobile learning framework that utilizes big data analytics technique to extract values from huge volume of mobile learners' data. Finally, we investigate learners' readiness and driving factors of mobile learning adoption in higher education institutions. In particular, we propose a hypothesized model for mobile learning adoption built on a locally extended technology acceptance model (TAM).

Experts and likely to be closed discussions in question and answer communities: An analytical overview

How do important members of online Question & Answer communities (who we call experts) behave? And how do they influence the discussions in which they take part? This work reports on an investigation into these questions, which we answer through analyses exploring metrics, machine learning classifiers, and recommendations. We report on several findings: the degree of expertise correlates to behavioral patterns, whereby experts would rarely ask for help, and instead, predominantly provide help to other community members; the inclusion of an expert results in longer discussions. We propose a metric (the weighted sum), which enables us to better quantify the reputations of expert members of the community. We describe the use of four machine learning classifiers for the identification of both expert users and the most significant conversations within these communities. We propose a novel approach for a recommendation system, which utilizes semantic annotations to identify topical experts and to ascertain their respective area of specialism. We foresee the suitability of our expertise-finding methods and findings to support Learning Analytics, and in scenarios where users may apply lessons learnt from our results to improve their status in a community. Our findings can also inform systems for recommending experts and discussions.