Learning assessment in the age of big data: Learning analytics in higher education

Caspari-Sadeghi, Sima

doi:10.1080/2331186x.2022.2162697

Cited by 25 publications

(9 citation statements)

References 46 publications

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“…Despite the lack of large-scale studies and real-world implementation issues of learning analytics-based processes (Hernández-de-Menéndez, 2022;Quadri & Shukor, 2021), there are high expectations about the potential benefits for stakeholders. Success hinges on properly addressing common issues regarding data privacy, infrastructure, trustworthiness, result accuracy and interpretability (Caspari-Sadeghi, 2023;El Alfy et al, 2019). Finally, the results of this study may serve as an exemplary case, illustrating how learning analytics can be leveraged to enhance understanding and inform decision-making in contemporary higher education through these automated data analysis workflows.…”

Section: Discussionmentioning

confidence: 88%

Surviving and thriving: How changes in teaching modalities influenced student satisfaction before, during and after COVID-19

Ortiz Beltrán,

Hernandez-Leo,

Amarasinghe

2023

AJET

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This paper leverages analytics methods to investigate the impact of changes in teaching modalities shaped by the COVID-19 pandemic on undergraduate students’ satisfaction within a Spanish brick-and-mortar higher education institution. Unlike research that has focused on faculty- or programme-level data, this study offers a comprehensive institutional perspective by analysing large-scale data (N = 83,532) gathered from satisfaction surveys across all undergraduate courses in eight faculties from 2018 to 2021. The longitudinal analysis revealed significant changes (p < 0.05) in satisfaction indicators, particularly overall satisfaction and perceived workload. During the emergency remote teaching period, there was a significant decrease in satisfaction and high levels of variability across courses. However, a year after emergency remote teaching, with increased implementations of technology-supported online and mixed teaching modalities, satisfaction measures not only recovered but exceeded pre-COVID levels in the aforementioned indicators when the teaching modality was fully co-located. The variability of answers also reached historical lows, reflecting more uniform student experiences. These findings highlight the resilience of educators and the current higher education system and suggest a capacity to learn and improve from disruptive pedagogical changes. The study also provides insights into how data analytics can help monitor and inform the evolution of teaching practices. Implications for practice or policy Higher education institution administrators should improve the understanding of the effects derived from changes in their teaching and learning models, for example, in teaching modalities and related technology support. Student satisfaction data analytics offer useful indicators to study the impact of those effects. Higher education institutions should provide support for educators to ensure minimal deviations from expected averages of educational quality indicators regardless of the educators’ capacity to adapt to changes in the teaching models.

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Section: Discussionmentioning

confidence: 88%

Surviving and thriving: How changes in teaching modalities influenced student satisfaction before, during and after COVID-19

Ortiz Beltrán,

Hernandez-Leo,

Amarasinghe

2023

AJET

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“…Another type could be a stealth assessment: a continuous, integrated, and inconspicuous method of evaluation that takes place in various forms (such as serious games, simulations, virtual labs, or forums). It involves collecting data on student performance while they engage in tasks (Caspari-Sadeghi, 2023).…”

Section: Humansmentioning

confidence: 99%

How are AI assistants changing higher education?

Schön

Neumann²,

Hofmann-Stölting

et al. 2023

Front. Comput. Sci.

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ContextHigher education is changing at an accelerating pace due to the widespread use of digital teaching and emerging technologies. In particular, AI assistants such as ChatGPT pose significant challenges for higher education institutions because they bring change to several areas, such as learning assessments or learning experiences.ObjectiveOur objective is to discuss the impact of AI assistants in the context of higher education, outline possible changes to the context, and present recommendations for adapting to change.MethodWe review related work and develop a conceptual structure that visualizes the role of AI assistants in higher education.ResultsThe conceptual structure distinguishes between humans, learning, organization, and disruptor, which guides our discussion regarding the implications of AI assistant usage in higher education. The discussion is based on evidence from related literature.ConclusionAI assistants will change the context of higher education in a disruptive manner, and the tipping point for this transformation has already been reached. It is in our hands to shape this transformation.

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“…As aspects of concreteness, virtual, remote, or hybrid education, educational innovation processes with ICT, digital transformation, and technological innovation find dynamizing aspects in the pedagogical models of STEAM from the recognition of interdisciplinary skills and the commitment of educational actors [82][83][84][85], education 4.0 [86][87][88][89][90][91][92][93], Big Data [94][95][96][97][98][99][100][101], Data Mining [102,103], Data Science [104][105][106][107][108][109][110][111], and Artificial Intelligence [112][113][114][115][116][117]. However, one should constantly reflect on the ethics, potentialities, and challenges of artificial intelligence in the framework of quality education [118].…”

Section: Educational Innovation and Ictmentioning

confidence: 99%

Trends in Educational Research for Sustainable Development in Postgraduate Education Programs at a University in Peru

et al. 2023

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The Sustainable Development Goals are part of the formative management in multiple universities that are committed to a better and more sustainable future for all; contributing to this from the postgraduate management in education involves rethinking educational research from lines that ensure that the research work has a real impact on the 2030 agenda. In this regard, the objective of this study was to identify and characterize specific lines of educational research for sustainable development in a private university in Lima, specifically in Postgraduate Education (Doctorate, Master’s and Second Specialty). The methodology deployed a qualitative approach, synchronizing the methods of systematization of experiences and grounded theory. A total of 186 students and 16 teachers participated, and the collection of information used three techniques: (1)—documentary review (5398 titles and other normative documents), (2)—asynchronous focus group (16 experienced teachers), and (3)—interview (202 participants). The results show four specific lines of research: (1)—Quality educational management, (2)—Teaching–learning–assessment, (3)—Educational innovation and ICT, and (4)—Training, research, and psychosocial factors in education. It concludes with a characterization of these lines based on an epistemic foundation that integrates current trends in educational research for sustainable development.

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Learning assessment in the age of big data: Learning analytics in higher education

Cited by 25 publications

References 46 publications

Surviving and thriving: How changes in teaching modalities influenced student satisfaction before, during and after COVID-19

Surviving and thriving: How changes in teaching modalities influenced student satisfaction before, during and after COVID-19

How are AI assistants changing higher education?

Trends in Educational Research for Sustainable Development in Postgraduate Education Programs at a University in Peru

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