Defining the Boundaries Between Artificial Intelligence in Education, Computer-Supported Collaborative Learning, Educational Data Mining, and Learning Analytics: A Need for Coherence

Rienties, Bart; Simonsen, Henrik Køhler; Herodotou, Christothea

doi:10.3389/feduc.2020.00128

Cited by 58 publications

(25 citation statements)

References 27 publications

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“…Based on their comprehensive systematic review they ask: "where are the educators?" This question has been raised before by Holmes et al (2019) and in other variants by Rienties et al (2020) and Simonsen (2020), and it is definitely about time that we discuss potential pedagogical approaches to artificial intelligence applications in higher education. The analysis of the nine cases used in this paper are related to the question raised.…”

Section: Introductionmentioning

confidence: 86%

“…That is unfortunate because we might miss the huge potential of AI applications without a clear pedagogical approach. Also Rienties et al (2020) call for increased cooperation between different disciplines to gain the full potential, and Simonsen (2020) points out that we have not come very far when it comes to the actual practical use of AI in higher education. In a more critical view, Balslev (2020) criticises the application of technology and AI in higher education and calls it "Evidence of a potential".…”

Section: Ai Applications and Pedagogy In Higher Educationmentioning

confidence: 99%

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Artificial Intelligence and Learning Activities: A Match Made in Heaven?

Simonsen

Almeida

2020

JECP

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Artificial Intelligence in Education (AIED) may be described as the next big disruptor in higher education, however, AIED still only remains “evidence of a potential” Balslev (2020). Practical experience with AI in higher education is very limited and potential pedagogical applications of AI has so far not been given much attention. The objective of this paper is to analyse and discuss concrete applications of AI to support different learning activities in higher education using the ABC Learning Design approach Young and Perovic (2016). The purpose of the paper is to contribute to research in the practical use of AI in higher education by presenting the AI Pedagogy Planner and to start the important theoretical discussion of AI applications from a pedagogical point of view. The paper is based on empirical data from nine selected cases of AI use in higher education in Portugal, the United Kingdom and Denmark, respectively. The analysis demonstrated that there is a need for new views on the pedagogical use of AI in higher education. However, the paper goes further and outlines an AI Pedagogy Planner combining six overall learning activities with eight types of AI applications.

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Section: Introductionmentioning

confidence: 86%

Section: Ai Applications and Pedagogy In Higher Educationmentioning

confidence: 99%

Artificial Intelligence and Learning Activities: A Match Made in Heaven?

Simonsen

Almeida

2020

JECP

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“…It is reported that use of AI-generated data through learning analytics and intelligent tutoring systems improve student performance [57]. Due to significant progress in theory and practice derived from the application of AI [12] and the potentials in educational data mining, learning analytics [50,58], it is further argued that AI is a future component of educational processes [34].…”

Section: T-sne Text-mining and Social Network Analysismentioning

confidence: 99%

Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

et al. 2021

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Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

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“…Even though it is still an emerging field, LA has already seen a plethora of trends and paradigm shifts and has been proved to have many benefits (Avella et al, 2016;Buckingham Shum & Crick, 2016) for all stakeholders including students, educators, researchers, institutions, and government agencies (Leitner et al, 2017;Peña-Ayala, 2018). Rienties et al (2020) defined LA as one of four approaches to understanding teaching and learning using technology, defining it as research into the challenges of collecting, analysing, and reporting data with the goal to improve learning processes. This differs from machine learning and artificial intelligence approaches that seek to understand and predict elements of the student experience, with LA focusing on more holistic approaches (Rienties et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Student Participation in Computing Studies to Understand Engagement and Grade Outcome

2021

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Aim/Purpose: This paper focuses on understanding undergraduate computing student-learning behaviour through reviewing their online activity in a university online learning management system (LMS), along with their grade outcome, across three subjects. A specific focus is on the activity of students who failed the computing subjects. Background: Between 2008 and 2020 there has been a multiplicative growth and adoption of Learning Analytics (LA) by education institutions across many countries. Insights gained through LA can result in actionable implementations at higher institutions for the benefit of students, including refinement of curriculum and assessment regimes, teacher reflection, and more targeted course offerings. Methodology: To understand student activity, this study utilised a quantitative approach to analyse LMS activity and grade outcome data drawn from three undergraduate computing subjects. Data analysis focused on presenting counts and averages to show an understanding of student activity. Contribution: This paper contributes a practical approach towards LA use in higher education, demonstrating how a review of student activity can impact the learning design of the computing subjects. In addition, this study has provided a focus on poor performing students so that future offerings of the computing subjects can support students who are at risk of failure. Findings: The study found that: • Collecting data relating to student activity and analysing the activity is an important indicator of engagement, with cross referencing the data to grade outcome providing information to support modification to the learning design of the computing subjects. • The computing subjects in this study all had the majority of the as-sessment marks awarded at the later part of the study period. • Students that fail subjects are active within the LMS for the period of the subject even when they submit no assessments • Assessment weight and the time of delivery could influence the out-comes Recommendations for Practitioners: The collection and analysis of student activity in the LMS can enable learning designers and practitioners to better reflect the subject design and delivery to provide more informed ways of delivering the learning material. Recommendation for Researchers: Collecting LA requires a thought-out process, designed well in advance of the teaching period. This study provides useful insight that can impact other researchers in the collection of assessment related analytics. Impact on Society: The cost of education is expensive to those that undertake it. Failing, although expected, potentially can be reduced by examining how education is designed, delivered, and assessed. The study has shown how information on how students are engaging has the potential to impact their outcomes. Future Research: Further work is needed to investigate whether intervention may assist the poor performing students to improve their grade outcomes relative to activity levels, subsequently impacting their retention.

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Defining the Boundaries Between Artificial Intelligence in Education, Computer-Supported Collaborative Learning, Educational Data Mining, and Learning Analytics: A Need for Coherence

Cited by 58 publications

References 27 publications

Artificial Intelligence and Learning Activities: A Match Made in Heaven?

Artificial Intelligence and Learning Activities: A Match Made in Heaven?

Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

Student Participation in Computing Studies to Understand Engagement and Grade Outcome

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