Be Careful What You Wish For! Learning Analytics and the Emergence of Data-Driven Practices in Higher Education

Pargman, Teresa Cerratto; McGrath, Cormac

doi:10.16993/bbk.i

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…The argument here is not that we should not measure individual performance, but a sole focus on individual knowing, will regardless of intention, downplay the importance of creative and collective learning, to teachers but maybe most problematic also to students. Besides aspects of measuring what we value, the very idea of continuous measurement has been critically discussed as it risks emphasizing completion of tests rather than developing critical thinking among students, and that it might create a sense of constant surveillance (Cerratto-Pargman and McGrath, 2021). The "right kind" (not all of them) of data are of critical importance to understand "how things are".…”

Section: Beyond Learning Analytics: Measuring What We Valuementioning

confidence: 99%

The challenge for the digital age: making learning a part of life

Fischer

Lundin

Lindberg

2022

IJILT

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PurposeThe main argument behind this paper is learning in the digital age should not be restricted to creating digital infrastructures for supporting current forms of learning nor taking schools in their current form as God-given, natural entities, but changing current forms of education by developing new frameworks and socio-technical environments for making learning an integral part of life. The authors provide a framework for this argumentation as well as a call-to-action for research on the co-evolution of learning, media, and learning organizations.Design/methodology/approachThis paper theoretically and argumentatively explores the core assumption that the digitalization of society results in challenges and opportunities for learning and education based on fundamental transformations (Collins and Halverson, 2009; Fischer et al., 2020).FindingsThe digital age greatly enhances the opportunities and supports the necessity for “making learning a part of life”. But while the growth of technology is certain, the inevitability of any particular future is not. The impact of schooling goes beyond that new information about computers, the Internet, and social media are integrated into the schools of today. The transformation of schools needs to be informed by an understanding of the impact of mindset formation that will determine people's approach to learning for the rest of their lives. The authors’ framework is focused on moving “beyond gift-wrapping” by not only fixing and existing systems but to change them and not only reforming but transforming them.Originality/valueIt is the authors’ hope that this article will be of interest to many stakeholders (including learners, teachers, curriculum designers, technology experts, parents, and politicians) and provide a foundation for an ongoing debate and informed actions for “Making Learning a Part of Life” in the digital age.

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Section: Beyond Learning Analytics: Measuring What We Valuementioning

confidence: 99%

The challenge for the digital age: making learning a part of life

Fischer

Lundin

Lindberg

2022

IJILT

View full text Add to dashboard Cite

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Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

et al. 2021

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Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching–learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching–learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers’ performances and recommendations based on data derived from students’ evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student’s recommendation for the teachers may be based on information about the students’ gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students’ recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed ( n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students’ comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments ( p = .000). While the males appear to be slightly borderline in terms of emotions ( p = .056) and sentiment ( p = .077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students’ recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1...

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