• Journal of The Korean Association For Science Education
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• v.33 no.5
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• pp.911-931
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• 2013

The purpose of this study was to analyze pre-service teachers' reflective discussion on their practical work-based teaching by focusing on the components of instruction and the connectivity of discussion. Eight after-class discussions were recorded and transcribed, and finally analyzed in terms of theoretically driven categories such as aims, teacher knowledge and learner response which also respectively reflect the actual flow of planning, implementation and evaluation of the teaching practice. The result showed that in their discussion about students, conceptual understanding and scientific skill components were most emphasized, while teaching method and strategy were most frequently addressed in the discussion about teacher knowledge. But this also revealed problems in their discussions such as the lack of discussion about inquiry and student interest, difficulties in clarifying theoretical terms and the lack of discussion about instructional models and theories. Meanwhile, pre-service teachers' discussions were limited in terms of connectivity between the components of instruction, meaning that their discussion tended to deal with each component separately rather than occurred in connection with each other. Furthermore, when connections were made during the discussion, only few components of instruction appeared. Based on this result, the paper suggests the need to develop tools to facilitate effective reflection in ways that incorporate various components of instruction and enhance connectivity between the components and between the instructions.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.