Curricular and Learning Analytics: A Big Data Perspective

Pinnell, Colin; Paulmani, Geetha; Kumar, Vive; Kinshuk, Kinshuk

doi:10.1007/978-3-319-06520-5_9

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…In addition, CAL and CBA contribute to a better understanding of their users since information about them and their interactions or learning patterns is gathered (Vaitsis et al, ). This approach is based on Learning Analytics' concepts, and advocates the measurement, collection, analysis, and reporting of data about learners and their contexts, to understand and optimize the learning process so as to enhance/personalize the learning experience (Siemens, ; Pinnell et al, ). Therefore, students' abilities, profiles and patterns which favor learning and knowledge acquisition can be traced, with the intent of creating learning strategies that will adapt to or improve their abilities (Ferguson, ; Siemens, ; Chatti et al, ).…”

Section: Introductionmentioning

confidence: 99%

The Role of Anatomy Computer‐Assisted Learning on Spatial Abilities of Medical Students

Guimarães

Firmino-Machado

Tsisar

et al. 2018

Anatomical Sciences Ed

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Currently, medical education context poses different challenges to anatomy, contributing to the introduction of new pedagogical approaches, such as computer-assisted learning (CAL). This approach provides insight into students' learning profiles and skills that enhance anatomy knowledge acquisition. To understand the influence of anatomy CAL on spatial abilities, a study was conducted. A total of 671 medical students attending Musculoskeletal (MA) and Cardiovascular Anatomy (CA) courses, were allocated to one of three groups (MA Group, CA Group, MA + CA Group). Students' pre-training and post-training spatial abilities were assessed through Mental Rotations Test (MRT), with scores ranging between 0-24. After CAL training sessions, students' spatial abilities performance improved (9.72 ± 4.79 vs. 17.05 ± 4.57, P < 0.001). Although male students in both MA Group and CA Group show better baseline spatial abilities, no sex differences were found after CAL training. The improvement in spatial abilities score between sessions (Delta MRT) was correlated with Musculoskeletal Anatomy training sessions in MA Group (r = 0.333, P < 0.001) and MA + CA Group (r = 0.342, P < 0.001), and with Cardiovascular Anatomy training sessions in CA Group (r = 0.461, P = 0.001) and MA + CA Group (r = 0.324, P = 0.001). Multiple linear regression models were used, considering the Delta MRT as dependent variable. An association of Delta MRT to the amount of CAL training and the baseline spatial abilities was observed. The results suggest that CAL training in anatomy has positive dose-dependent effect on spatial abilities. Anat Sci Educ 00: 000-000. © 2018 American Association of Anatomists.

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Section: Introductionmentioning

confidence: 99%

The Role of Anatomy Computer‐Assisted Learning on Spatial Abilities of Medical Students

Guimarães

Firmino-Machado

Tsisar

et al. 2018

Anatomical Sciences Ed

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“…Learning analytics (LA) is a growing research area that focuses on measuring, collecting, analyzing, and reporting data about students and their learning processes (Viberg et al, 2018). Curricular analytics (CA) has emerged as a subfield of LA that aims to use evidence to drive curriculum decision-making and program improvement (Pinnell et al, 2017). The ability to analyze data at the population level (Figlio et al, 2016) is a characteristic of CA that enhances the internal validity of the findings.…”

Section: Methodological Approachmentioning

confidence: 99%

Implications of losing a need- and merit-based scholarship on the educational trajectory: a curricular analytics approach

Salazar-Fernandez,

Munoz-Gama,

Sepúlveda

2024

High Educ

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Understanding how students with low socioeconomic status finance their tuition over time can help us comprehend the impact of students’ decisions on their subsequent curricular progress, graduation, or dropout. This work presents a curricular analytics approach using process mining techniques to study educational funding trajectories as processes. Specifically, the SCHOLARSHIP-LOAN-SELF-FUNDED model is designed to reveal educational funding trajectories and obtain aggregate information. Academic and tuition records of 2484 undergraduate students from a private Chilean university who started their programs with a government need- and merit-based tuition aid were analyzed. Students who lost their scholarships were more likely to drop out, whereas students who maintained this aid were more likely to graduate on time. Curricular progress per semester was slower after scholarships ended or after the students lost them and stayed. Financial aid was associated with students’ curricular progress and linked to their permanence and graduation time. Higher education institutions should consider the eligibility criteria and maintenance requirements of financial assistance when designing their curricula.

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“…Learning analytics is a combination of method and technique (Papamitsiou and Economides 2014). Pinnell et al (2017) discuss the differences between learning analytics and analysis and explain the ways in which data analysis is linked to a specific process of posing research questions, gathering data, running a statistical test, and providing evidence to support claims. The authors write that "analytics exists overtop of this view of analysis towards insights" (Pinnell et al 2017, 129) in a process that departs from traditional methodological considerations.…”

Section: Learning Analytics Librarianship and Core Competenciesmentioning

confidence: 99%

Conflicting Values: An Exploration of the Tensions between Learning Analytics and Academic Librarianship

Oliphant¹,

Brundin²

2019

Library Trends

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The prevailing rhetoric concerning learning analytics is that its use will support the educational endeavor and make significant improvements to teaching and learning. For academic libraries, learning analytics presents the possibility of using library data to coordinate, integrate, and align with the goals of the institutions in which they are embedded. While libraries have a long history of collecting data to support various service and learning objectives, those data have typically been siloed, de-identified, private, and confidential. Although there are positive contributions that learning analytics can make to the learning process, there are concerns associated with its use, particularly the tensions between the objectives of learning analytics contrasted with different conceptualizations of learners and the values of education and librarianship. Institutions of higher education use learning analytics to achieve institutionally defined goals and outcomes for students, which creates tensions with the enshrined values of the American Library Association's Code of Ethics, Library Bill of Rights, and Core Competencies of Librarianship. The transcendent benefits to society that are inherent in education and academic librarianship, such as the rights and responsibilities of citizenship, are not measurable through learning analytics.

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Curricular and Learning Analytics: A Big Data Perspective

Cited by 6 publications

References 17 publications

The Role of Anatomy Computer‐Assisted Learning on Spatial Abilities of Medical Students

The Role of Anatomy Computer‐Assisted Learning on Spatial Abilities of Medical Students

Implications of losing a need- and merit-based scholarship on the educational trajectory: a curricular analytics approach

Conflicting Values: An Exploration of the Tensions between Learning Analytics and Academic Librarianship

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