Predictive Modelling in Teaching and Learning

Brooks, Christopher; Thompson, Craig

doi:10.18608/hla17.005

Cited by 64 publications

(40 citation statements)

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“…The selection of predictors in a predictive model varies from an explanatory model, as it tries to include all information that can possibly add to the predictability [ 60 , 61 ]. A stepwise backward logistic regression was performed to find how far using SNA indicators can successfully classify achievers and low-achievers.…”

Section: Resultsmentioning

confidence: 99%

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Using social network analysis to understand online Problem-Based Learning and predict performance

2018

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Social network analysis (SNA) may be of significant value in studying online collaborative learning. SNA can enhance our understanding of the collaborative process, predict the under-achievers by means of learning analytics, and uncover the role dynamics of learners and teachers alike. As such, it constitutes an obvious opportunity to improve learning, inform teachers and stakeholders. Besides, it can facilitate data-driven support services for students. This study included four courses at Qassim University. Online interaction data were collected and processed following a standard data mining technique. The SNA parameters relevant to knowledge sharing and construction were calculated on the individual and the group level. The analysis included quantitative network analysis and visualization, correlation tests as well as predictive and explanatory regression models. Our results showed a consistent moderate to strong positive correlation between performance, interaction parameters and students’ centrality measures across all the studied courses, regardless of the subject matter. In each of the studied courses, students with stronger ties to prominent peers (better social capital) in small interactive and cohesive groups tended to do better. The results of correlation tests were confirmed using regression tests, which were validated using a next year dataset. Using SNA indicators, we were able to classify students according to achievement with high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with reasonable reliability, which is an obvious opportunity for intervention and support.

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Section: Resultsmentioning

confidence: 99%

“…To validate the results, a next year data-set of the same four courses were used. For an in-depth review of the predictive models in education, please refer to reference [ 48 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

Using social network analysis to understand online Problem-Based Learning and predict performance

2018

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“…Most MOOC research has exploited a posteriori student data to understand the learning behaviour of MOOC participants (Kizilcec et al, 2013;Tseng, Tsao, Yu, Chan, & Lai, 2016) and to identify the variables that can explain learners' engagement and success Er, Bote-Lorenzo, Gómez-Sánchez, Dimitriadis, & Asensio-Pérez, 2017). These research studies have substantial value in establishing the theoretical foundations of how individuals behave, engage, and learn at massive and open learning settings (Brooks & Thompson, 2017;Liu & Koedinger, 2017). However, their impact in the practice of MOOC teaching and learning is still questionable.…”

Section: Background: Learning Design Learning Analytics and Moocsmentioning

confidence: 99%

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Gómez‐Sánchez

Dimitriadis

et al. 2019

Interactive Learning Environments

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With the emergence of MOOCs, there is a growing interest in prediction research. Most existing predictive models do not consider the context for which they are intended, thus resulting in limited impact. Learning design (LD) can provide a contextual understanding for the design of predictive models in collaboration with the instructors, maximizing their potential for supporting learning. This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning LD and LA during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past MOOC, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, including the learning activity to focus on, the target variable to predict, and the practical constraints to consider. Later, two predictive models were built for the prediction task identified: LD-specific model, in which the features were based on the LD and pedagogical intentions, and a generic model, which was based on cumulative features, not informed by the LD. Although the LD-specific predictive model did not outperform the generic one, some features derived from the LD and pedagogical intentions were predictive. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor's opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the parts of the LD that need improvement. That is, the results of the LA informed back the LD, where the instructor was a mediator. The implications for improving the LD are discussed.

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“…a course) to establish a predictive model. For example, the analysis of LMS and student demographic data to predict student academic performance, attrition or concept understanding (Brooks and Thompson, 2017). Purdue Course Signals is a well-noted example of such an Early Warning Systems attempting to detect students at risk of academic failure (Arnold and Pistilli, 2012;Krumm et al, 2014).…”

Section: Common Learning Analytics Themesmentioning

confidence: 99%

How do we start? An approach to learning analytics adoption in higher education

Gašević

Tsai

Dawson

et al. 2019

IJILT

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Purpose The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics? Design/methodology/approach A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education. Findings The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. Originality/value The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

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Predictive Modelling in Teaching and Learning

Cited by 64 publications

References 0 publications

Using social network analysis to understand online Problem-Based Learning and predict performance

Using social network analysis to understand online Problem-Based Learning and predict performance

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

How do we start? An approach to learning analytics adoption in higher education

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