A Landscape of Learning Analytics: An Exercise to Highlight the Nature of an Emergent Field

Peña-Ayala, Alejandro; Cárdenas-Robledo, Leonor Adriana; Sossa, Humberto

doi:10.1007/978-3-319-52977-6_3

Cited by 10 publications

(22 citation statements)

References 114 publications

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“…With respect to the underlying factors, it is necessary to be concerned for the LA labor according to legal practices that follow ethical and privacy guides, apply lawful terms, and protect data to assure the respectful use of the students' information according to their rights. In contrast with Peña‐Ayala et al (), this review presents a moderate number of theoretical works, where most correspond to frameworks, whilst none propose theories, models, or techniques. Moreover, SRL and collaborative learning are the most common paradigms being the object of study by LA approaches, as well as NLP, discourse analytics, and social LA are the most tackled settings.…”

Section: Discussionmentioning

confidence: 86%

“…In Peña‐Ayala et al () a three tier LA Taxonomy is proposed to organize the work reported in 2014 up to the first quarter of 2016, where a glimpse of LA represents the first branch of study, the learning surroundings depict the learning, theoretical, and legal concerns of LA, and four kinds of applications are highlighted. In this sense, the current review represents “a second chapter” that refreshes and complements the LA landscape.…”

Section: Profilementioning

confidence: 99%

“…Even though LA is a novel field, a chronicle of how the term was coined is given in Peña‐Ayala, Cardenas‐Robledo, and Sossa (), where a briefing presented by Mitchel and Costello () in 2000 asserts: “The e‐learning industry, now in its infancy, will grow into adulthood after 2005… will see key opportunities in … tools for LA …”. Thus, LA is the field devoted to provide a meaning of educational labor carried out in diverse settings.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, LA is the field devoted to provide a meaning of educational labor carried out in diverse settings. Furthermore, in Peña‐Ayala et al () it is said: The LA underlying domain is composed of computers in education (Pérez‐Sanagustín et al, ), computer‐based information systems (Peppard & Ward, ), and knowledge‐based systems (Castro‐Schez, Gallardo, Miguel, & Vallejo, ). In essence, LA examines the information generated by CBES to uncover and interpret the hidden knowledge that contributes to understand, regulate, and enhance the educational labor.…”

Section: Introductionmentioning

confidence: 99%

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Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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Section: Discussionmentioning

confidence: 86%

Section: Profilementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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“…With the prominence of big data analytics, the Horizon Report: Edition 2016 proposed learning analytics to become the future trend in education [2]. Learning analytics is a process of measuring and analyzing learning data collected from learning environments [3], [4], [5]. Predicting students' learning performance is one of the main research topics in learning analytics.…”

Section: Introductionmentioning

confidence: 99%

Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

Yang

Huang

et al. 2018

Journal of Information Processing

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Abstract:With the rise of big data analytics, learning analytics has become a major trend for improving the quality of education. Learning analytics is a methodology for helping students to succeed in the classroom; the principle is to predict student's academic performance at an early stage and thus provide them with timely assistance. Accordingly, this study used multiple linear regression (MLR), a popular method of predicting students' academic performance, to establish a prediction model. Moreover, we combined MLR with principal component analysis (PCA) to improve the predictive accuracy of the model. Traditional MLR has certain drawbacks; specifically, the coefficient of determination (R 2 ) and mean square error (MSE) measures and the quantile-quantile plot (Q-Q plot) technique cannot evaluate the predictive performance and accuracy of MLR. Therefore, we propose predictive MSE (pMSE) and predictive mean absolute percentage correction (pMAPC) measures for determining the predictive performance and accuracy of the regression model, respectively. Analysis results revealed that the proposed model for predicting students' academic performance could obtain optimal pMSE and pMAPC values by using six components obtained from PCA.

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Reviewing Mixed Methods Approaches Using Social Network Analysis for Learning and Education

Froehlich

Rehm

Rienties

2019

Lecture Notes in Social Networks

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Across the globe researchers are using social network analysis (SNA) to better understand the visible and invisible relations between people. While substantial progress has been made in the last 20 years in terms of quantitative modelling and processing techniques of SNA, there is an increased call for SNA researchers to embrace and mix methods developed in qualitative research to understand the what, how, and why questions of social network relations. In this chapter, we will reflect on our experiences with our latest edited book called "Mixed Methods Approaches to Social Network Analysis for Learning and Education", which contained contributions from 20+ authors. We will first review the empirical literature of Mixed Methods Social Network Analysis (MMSNA) by conducting a systematic literature review. Secondly, by using two case-studies from our own practice we will critically reflect on how we have used MMSNA approaches. Finally, we will discuss the potential limitations of MMSNA approaches, in particular given the complexities of mastering two ontologically different methods.

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A Landscape of Learning Analytics: An Exercise to Highlight the Nature of an Emergent Field

Cited by 10 publications

References 114 publications

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

Reviewing Mixed Methods Approaches Using Social Network Analysis for Learning and Education

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