Using log data of virtual learning environments to examine the effectiveness of online learning for teacher education in Rwanda

Karunaratne, Thashmee; Byungura, Jean Claude

doi:10.23919/istafrica.2017.8102410

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…Behavior when using AI and LA [31,35,39,40,42,44,52] 7 Digital competence [34,38,45,50,53,58] 6 Perception of AI and LA [32,41,48,56,60] 5 Self-regulation and reflection [33,35,47,54,55] 5 Engagement [36,51,57,59] 4 Analysis of educational data [37,43,49] 3…”

Section: Goals and Objectives Reference Number Number Of Studiesmentioning

confidence: 99%

“…The findings indicate that the most prominent category regarding the goals and objectives of the selected studies is the pre-and in-service teachers' behavior when using AI and LA. For example, some of the studies include the visualization of the behaviors and interactions [31,40,52], where AI and LA tools help to explore the effectiveness of the learning activities offered. This is important, because the behavior of pre-and in-service teachers has received an increased focus, due to its relation to teacher preparation and professional training [61].…”

Section: Goals and Objectives Reference Number Number Of Studiesmentioning

confidence: 99%

“…Behavioral data Access data [32,35,36,38,40,[46][47][48][49]56,60] Social interaction data [31,44,56] Discourse data…”

Section: Data Sources Types Of Datasets Reference Numbermentioning

confidence: 99%

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Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Salas‐Pilco

Xiao

2022

Education Sciences

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In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced into the field of education, where their use has great potential to enhance the teaching and learning processes. Researchers have focused on applying these technologies to teacher education, as they see the value of technology for educating. Therefore, a systematic review of the literature on AI and LA in teacher education is necessary to understand their impact in the field. Our methodology follows the PRISMA guidelines, and 30 studies related to teacher education were identified. This review analyzes and discusses the several ways in which AI and LA are being integrated in teacher education based on the studies’ goals, participants, data sources, and the tools used to enhance teaching and learning activities. The findings indicate that (a) there is a focus on studying the behaviors, perceptions, and digital competence of pre- and in-service teachers regarding the use of AI and LA in their teaching practices; (b) the main data sources are behavioral data, discourse data, and statistical data; (c) machine learning algorithms are employed in most of the studies; and (d) the ethical clearance is mentioned by few studies. The implications will be valuable for teachers and educational authorities, informing their decisions regarding the effective use of AI and LA technologies to support teacher education.

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Section: Goals and Objectives Reference Number Number Of Studiesmentioning

confidence: 99%

Section: Goals and Objectives Reference Number Number Of Studiesmentioning

confidence: 99%

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Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Salas‐Pilco

Xiao

2022

Education Sciences

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“…These attempts are currently expanding in higher educational settings. For instance, a number of research directions have been explored with a learning analytics approach by a research group at Stockholm University, focusing on aspects such as problem-based learning [60], teacher education [61], collaborative learning [62], self-regulated behavior [63], prediction of student performance in blended learning and in flipped classroom settings [64], and prediction of performance and completion of master-and bachelor thesis [65].…”

Section: Swedenmentioning

confidence: 99%

Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

Nouri

Ebner²,

Saqr

et al. 2019

iJAI

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Information and communication technologies are increasingly mediating learning and teaching practices as well as how educational institutions are handling their administrative work. As such, students and teachers are leaving large amounts of digital footprints and traces in various educational apps and learning management platforms, and educational administrators register various processes and outcomes in digital administrative systems. It is against such a background we in recent years have seen the emergence of the fast-growing and multi-disciplinary field of learning analytics. In this paper, we examine the research efforts that have been conducted in the field of learning analytics in Austria, Denmark, Finland, Norway, Germany, Spain, and Sweden. More specifically, we report on developed national policies, infrastructures and competence centers, as well as major research projects and developed research strands within the selected countries. The main conclusions of this paper are that the work of researchers around Europe has not led to national adoption or European level strategies for learning analytics. Furthermore, most countries have not established national policies for learners’ data or guidelines that govern the ethical usage of data in research or education. We also conclude, that learning analytics research on pre-university level to high extent have been overlooked. In the same vein, learning analytics has not received enough focus form national and European national bodies. Such funding is necessary for taking steps towards data-driven development of education.

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“…Digital learning systems collect a vast amount of data, which is being analysed by researchers of learning analytics and educational data mining (Ihantola et al, 2015;Gray, 2014). Different approaches to analysing learning data have gained insights about learner engagement in online courses Karunaratne and Byungura, 2017), shown methods for differentiating between high and low performing students by using click logs (Tedre and Verhagen, 2014), helped to build predictive models to explain performance of students (Nouri et al, 2019; and has resulted in knowledge about various visualisation techniques (Vesin et al, 2018) and the relevance of sensors such as eye-trackers in learning (Ivanović et al, 2017).…”

Section: Learning Analyticsmentioning

confidence: 99%

Digital learning approaches in an intermediate-level computer science course

Apiola

Lokkila

Laakso

2019

IJILT

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Purpose Digital learning has become a global trend. Partly or fully automatic learning systems are integrated into education in schools and universities on a previously unseen scale. Learning systems have a lot of potential for re-education, life-long learning and for increasing access to educational resources. Learning systems create massive amounts of data about learning behaviour. Analysing that data for educational decision making has become an important track of research. The purpose of this paper is to analyse data from an intermediate-level computer science course, which was taught to 141 students in spring 2018 at University of Turku, Department of Future Technologies, Finland. Design/methodology/approach The available variables included number of submissions, submission times, variables of groupwork and final grades. Associations between these variables were looked at to reveal patterns in students’ learning behaviour. Findings It was found that time usage differs per different grades so that students with grade 4 out of 5 used most time. Also, it was found that studying at night is connected to weaker learning outcomes than studying during daytime. Several issues in relation to groupwork were revealed. For example, associations were found between prior skills, preference for individual vs groupwork, and course learning outcomes. Research limitations/implications The research was limited by the domain of available variables, which is a common limitation in learning analytics research. Practical implications The practical implications include important ideas for future research and interventions in digital learning. Social implications The importance of research on soft skills, social skills and collaboration is highlighted. Originality/value The paper points a number of important ideas for future research. One important observation is that some research questions in learning analytics need qualitative approaches, which need to be added to the toolbox of learning analytics research.

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Using log data of virtual learning environments to examine the effectiveness of online learning for teacher education in Rwanda

Cited by 5 publications

References 15 publications

Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

Digital learning approaches in an intermediate-level computer science course

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