Data-Driven Validation of Pedagogical Model - A Case of Blended LCM Model

Kuromiya, Hiroyuki; Majumdar, Rwiajit; Warriem, Jayakrishnan Madathil; Ogata, Hiroaki

doi:10.1109/t4e.2019.00016

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The authors have previously reported the preliminary observations on adaption of LCM pedagogy in context to blended Physics course (Kannan & Gouripeddi, 2019). Further, a case study of the data-driven validation of the blended Physics course were reported using the learning logs of students (Kuromiya, Majumdar, Warriem, & Ogata, 2019). The present work mainly focuses on orchestrating the LCM pedagogy with the support of learning analytics (LAView) dashboard tool of TEEL (Technology-enhanced and Evidence-based Education and Learning) platform.…”

Section: Motivationmentioning

confidence: 99%

Flip & Pair – a strategy to augment a blended course with active-learning components: effects on engagement and learning

Kannan

Kuromiya

Gouripeddi

et al. 2020

Smart Learn. Environ.

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Blended learning technique has adapted many new digitized tools to facilitate students with flexible and self-phased learning opportunities. The flipped classroom strategy, one of the blended learning models has been limited by low engagement of students in the online component. In the present study, we augment a Flip and Pair (F&P), an active-learning strategy into the blended learning course. Following the AB type single group quasi-experimental design, we evaluated the effects of F&P strategy on the student’s engagement and learning while orchestrating it for an undergraduate engineering physics course. Our results highlighted that there is a positive correlation between the engagement (computed based on learning logs of TEEL (Technology-enhanced and Evidence-based Education and Learning) platform in the F&P activities with that of the performance score (knowledge quizzes and final exam). F&P strategy had a better contribution compared to Flip and Traditional Teaching (F&TT) strategy with respect to both engagement and performance. Also, students exhibited a positive perception of learning and engagement. Based on our findings, we identified the key instructional measures that an instructor can follow to increase student engagement while using the F&P strategy.

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Section: Motivationmentioning

confidence: 99%

Flip & Pair – a strategy to augment a blended course with active-learning components: effects on engagement and learning

Kannan

Kuromiya

Gouripeddi

et al. 2020

Smart Learn. Environ.

Self Cite

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“…A major outcome of this effort was that we could identify instructors who wanted to engage in a research-practice partnership. In the initial phase of the study, the instructor developed a blended learning pedagogy that used an active learning strategy along with BookRoll to improve learners' engagement in an undergraduate Physics class [41], [42] . A key finding of this study was the need for external motivation by instructors to initiate learners to use the technology for active learning.…”

Section: Indiamentioning

confidence: 99%

Learning and Evidence Analytics Framework (LEAF): Research and Practice in International Collaboration

Ogata

Majumdar

Yang

et al. 2022

ITEL

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Learning analytics (LA) is maturing as a research discipline in its own terms, focusing on multiple perspectives of data-informed understanding and supporting teaching and learning activities in different educational contexts. With the development of learning technology platforms, it is now possible to gather users' interaction traces in a standardized format as teaching and learning logs during such educational activities. At the Learning and Educational Technology Research Unit at Kyoto University, we developed a learning and evidence analytics framework (LEAF), an integrated technology framework that incorporates methods and tools in learning platforms that are implemented at an institutional level. We conducted research to investigate learning episodes and their effects to systematically inform practice. In this study, we present LEAF and the overall approach of the research and outreach that was achieved toward supporting evidence-based practices in education with the LA framework within Japan and internationally, with collaborators from Taiwan and India. LEAF enables a sustainable way to continue collecting learning logs, create data-driven services to support and improve teaching and learning practices while collaborating with researchers to share data and report internationally co-authored papers.

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“…• Ensemble StackingC • Basic classifiers • Validation based on pedagogical model data -the case of a mixed LCM model • Transactions in neural networks and training systems • Pedagogical assessment in the era of machine-mediated learning • Applying relevant academic and personality characteristics from large unstructured data to identify good and poorly suited students • Built-in regression of fuzzy clustering to predict student performance [31][32][33][34] The present study results, in comparison with similar studies by foreign authors, differ in:…”

Section: Fig 2 Error Matrices For the Cases Of Applying The Random Forest Classifiermentioning

confidence: 99%

Machine Learning Methods and Qualimetric Approach to Determine the Conditions for Train Students in the Field of Environmental and Economic Activities

Саламатов

Гафарова

Belevitin

et al. 2021

Int. J. Emerg. Technol. Learn.

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The relevance of environmental and economic activity requires professional training of specialists and, accordingly, new organizational and pedagogical conditions for effective education. It is also necessary to develop control and measuring materials that would have all the qualities (validity, reliability, consistency, significance and objectivity) to obtain the most reliable results in justifying the need and sufficiency of the identified conditions. The intensification of information processes in vocational education leads researchers to the need to find optimal conditions and tools to achieve pedagogical goals. Among these tools are machine learning methods and mathematical models built on their basis for quantitative assessment of the quality of vocational training in the field of environmental and economic activities. The use of the qualimetric approach in pedagogy is possible in the presence of a certain array of observational data for one or another criterion related to learning conditions, personal qualities of students, etc. The construction of an algorithmic model allows one to operate with conditions in mental experiments, test hypotheses, and since pedagogical research is quite long in time, the choice of conditions based on the most favorable forecast built using the model allows one to optimize pedagogical resources to achieve the planned results. Rational selection of effective control and measuring materials (CMMs) allows one to determine the need and sufficiency of organizational and pedagogical conditions. While mathematical modeling allows one to quickly adjust the organizational and pedagogical conditions as a set of opportunities for content, forms, teaching methods, information and communication technologies (ICTs) and CMMs used to achieve the planned educational results in the sphere of environmental and economic activity. Interpretation of the derived features in the context of the pedagogical research performed with a cross-validation accuracy of 72% made it possible to reveal the dominant significance of intersubjective connections between the disciplines studied by the sample of students in the bachelor's and master's programs. Namely, programs 44.03.04 and 44.04.04 "Professional training (by industry)", which are the most significant in terms of the formation of competence in the field of environmental and economic activities. The designed mathematical model of the Gradient Boosting Classifier allows making predictive expectations of the studied competency types and testing hypotheses for the inclusion or exclusion of certain significant organizational and pedagogical conditions for the effective implementation of the educational process. A necessary and sufficient organizational and pedagogical condition for the effective formation of competence in the field of environmental and economic activity is to ensure continuity between significant disciplines and the actualization of interdisciplinary relationships based on the development of interdisciplinary courses.

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Data-Driven Validation of Pedagogical Model - A Case of Blended LCM Model

Cited by 4 publications

References 13 publications

Flip & Pair – a strategy to augment a blended course with active-learning components: effects on engagement and learning

Flip & Pair – a strategy to augment a blended course with active-learning components: effects on engagement and learning

Learning and Evidence Analytics Framework (LEAF): Research and Practice in International Collaboration

Machine Learning Methods and Qualimetric Approach to Determine the Conditions for Train Students in the Field of Environmental and Economic Activities

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