Mining school teachers' MOOC training responses to infer their face-to-face teaching strategy preference

Abstract

PurposeThe general goal of this paper is to help educators understand the importance of MOOC training to school teachers and their hypothetical value for predicting the use of teaching strategies in the face-to face-classroom teaching. With this purpose, the study is guided by two research questions: (1) Are there different patterns of preferences in teaching strategies among school teachers when they participate in MOOC training? (2) To what extent the attributes selected from the data set to visualize patterns are suitable for the formation of models?Design/methodology/approachPeer instruction (PI) and think-pair-share (TPS) strategies might bring positive outcome during classroom teaching. When introduced properly to school teachers, these strategies help students see reason beyond the answers by sharing with other students their response and thus learning from each other. This study aims to use educational data mining (EDM) techniques to visualize patterns and propose models based on the teaching strategies training to be used in face-to-face classroom teaching. The data set includes five attributes extracted from school teachers' Massive Open Online Courses (MOOC) training interaction data. All analysis and visualization were performed using Python, and the models were evaluated using fivefold cross-validation. The modeling performance of three different algorithms (decision tree, random forest and K-means) was tested on the data set. The results of model accuracy were presented as a confusion matrix. The experimental results indicate that the random forest (RF) algorithm outperforms decision tree (DT) and K-means algorithms with an accuracy of 96.4%.FindingsThis visualization information on the grouping of school teachers based on the teaching strategies serves as an essential reference for school teachers choosing between the two types of strategies within their face-to-face classroom settings. Teachers may use the finding obtained for an initial understanding of which strategies will fit well on their classroom teaching based on their subject majors. Moreover, the classification accuracy rates of DT and RF algorithms were the highest and considered highly significant to allow developing predictive models for similar EDM cases and provide a positive effect on the learning environment.Research limitations/implicationsThis visualization information on the grouping of school teachers based on the teaching strategies serves as an essential reference for school teachers choosing between the two types of strategies within their face-to-face classroom settings. Teachers may use the finding obtained for an initial understanding of which strategies will fit well on their classroom teaching based on their subject majors. Unlike predicting different patterns of preferences in teaching strategies among school teachers when they participate in MOOC training, using visualization was found much more comfortable, less complicated and more time-efficient for small data sets. Moreover, the classification accuracy rates of decision tree and random forest algorithms were the highest and considered highly significant to allow developing predictive models for similar educational data mining cases and provide a positive effect on the learning environment.Practical implicationsDT classifier in this study ranks first before model optimization, but second after model optimization in terms of accuracy. Therefore, the goodness of the indicators needs to be further studied to devise a reasonable intervention.Social implicationsA different group of school teachers attending training on teaching strategies in a different online platform is required in future research to cross-validate these study findings.Originality/valueThe authors declare that this submission is their own work and to the best of their knowledge it contains no materials previously published or written by another person, or substantial proportions of material that have been accepted for the award of any other degree at any other educational institution.