Implementing learning analytics for learning impact: Taking tools to task

Knight, Simon; Gibson, Andrew; Shibani, Antonette

doi:10.1016/j.iheduc.2020.100729

Cited by 45 publications

(31 citation statements)

References 45 publications

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“…The increasing use of data and technology in education enables to implement strategies for analysing student academic success [22]. In this line, there are a growing number of studies on the use of Learning Analytics for "understanding and optimising learning and the environments in which it occurs" [15].…”

Section: Evaluation Of Teaching Experiencementioning

confidence: 99%

Introducing innovative technologies in higher education: An experience in using geographic information systems for the teaching‐learning process

Pérez-delHoyo

Mora

Ciriquián

et al. 2020

Comp Applic In Engineering

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In today's world, new technologies are being used for the teaching‐learning process in the classroom. Their use to support learning can provide significant advantages for the teaching‐learning process and have potential benefits for students, as many of these technologies are a part of the work life of many current professions. The aim of this study is to analyse the use of innovative technologies for engineering and science education after examining the data obtained from students in their learning process and experiences. The study has been focused on computational geographic information systems, which allow access to and management of large volumes of information and data, and on the assessment of this tool as a basis for a suitable methodology to enhance the teaching‐learning process, taking into account the great social impact of big data. The results allow identifying the main advantages, opportunities, and drawbacks of using these technological tools for educational purposes. Finally, a set of initiatives has been proposed to complement the teaching activity and to improve user experience in the educational field.

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Section: Evaluation Of Teaching Experiencementioning

confidence: 99%

Introducing innovative technologies in higher education: An experience in using geographic information systems for the teaching‐learning process

Pérez-delHoyo

Mora

Ciriquián

et al. 2020

Comp Applic In Engineering

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“…The ever-increasing use of data and technology in the education environment has opened new research areas, including Educational Data Mining (EDM), Artificial Intelligence (AI), and Learning Analytics (LA) to effectively manage and use data throughout learning processes (Knight et al, 2020). E-learning refers to IT-based learning and has had a significant impact on educational institutions' quality of learning outcomes (ZareRavasan & Ashrafi, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

2021

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Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements.

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“…This special issue focuses on how to improve universal access to educational data, with emphasis on (a) new technologies and associated data in educational contexts: artificial intelligence systems [70], robotics [71][72][73], augmented [74][75][76] and virtual reality (VR) [77][78][79][80][81], and educational data integration and management [82]; (b) the role of data in the digital transformation and future of higher education: Personal Learning Environments (PLE) [83,84], mobile PLE [85,86], stealth assessment [87], technology-supported collaboration and teamwork in educational environments [88], and student's engagement and interactions [89,90]; (c) user and case studies on ICTs in education [91,92]; (d) educational data in serious games and gamification: gamification design [93][94][95][96], serious game mechanics for education [97,98], ubiquitous/pervasive gaming [99], and game-based learning and teaching programming [100,101]; and (e) educational data visualization and data mining [102]: learning analytics [103], knowledge discovery [104], user experience [105,106], social impact [107], good practices [108], and accessibility …”

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confidence: 99%

New methods and technologies for enhancing usability and accessibility of educational data

Fonseca

García-Peñalvo

Camba

2020

Univ Access Inf Soc

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Recent advances in information and communication technologies (ICTs) have fostered the development of new methods and tools for exploring the increasingly large amounts of data that come from pedagogical domains [1][2][3][4][5]. These data have the potential to transform education into a personalized experience [6, 7] that meets the needs of each individual student [8]. Educational data research is becoming highly relevant in massive online courses [9], especially MOOCs (Massive Open Online Courses) [10-13] and SPOCs (Small Private Online Courses) [14][15][16]. Educational data are also the basis for learning analytics [17][18][19], with an increasing focus on the way educational data are presented [20][21][22], how users interact with the data [23][24][25][26], and data privacy and security [27][28][29][30].There are many types of data that can support student's learning [31], but the type and nature of the data, how they can be accessed, and who can access them, vary significantly. Whether educational data are collected from collaborative learning environments [32][33][34], course management systems [35,36], gamified training applications [37,38], or administrative systems from schools and universities [39-41], valuable properties, patterns, and insights often emerge. When combined with other factors such as timing and context, these factors play an important role in understanding how students learn [42], the settings in

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Implementing learning analytics for learning impact: Taking tools to task

Cited by 45 publications

References 45 publications

Introducing innovative technologies in higher education: An experience in using geographic information systems for the teaching‐learning process

Introducing innovative technologies in higher education: An experience in using geographic information systems for the teaching‐learning process

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

New methods and technologies for enhancing usability and accessibility of educational data

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