Exploring Relations Among Pre-Service Science Teachers’ Motivational Beliefs, Learning Strategies and Constructivist Learning Environment Perceptions Through Unsupervised Data Mining

Abstract

Educational data mining is a developing research trend for exploring hidden patterns and natural associations among a set of student, teacher or school related variables. Discovering profiles of preservice science teachers using data mining methods would give important information about quality of teacher education programs and future science teachers’ performance. The aim of this research was to describe characteristics of preservice science teachers and to explore the relations among their motivational beliefs, learning strategy use, and constructivist learning environment perceptions. Participants included 480 preservice science teachers in their final semester of the teacher education program. Data were gathered using Demographic Questionnaire, Motivated Strategies for Learning Questionnaire, Achievement Goal Questionnaire and Constructivist Learning Environment Scale. Findings of clustering analysis revealed gender as a discriminating factor between the obtained two natural groups. Preservice science teachers’ characteristics including background characteristics, motivational beliefs, strategy use and constructivist learning environment perceptions were grouped into two clusters, namely males and females. Moreover, the association rules mining analysis revealed strong relations among preservice science teachers’ motivational beliefs, learning strategy use, and constructivist learning environment perceptions. This research provided many important findings that can be useful for further decision-making strategies. Keywords: constructivist learning environment, data mining, learning strategies, motivational belief, pre-service science teacher

Contributions of Data Mining to University Education, in the Context of the Covid-19 Pandemic: A Systematic Review of the Literature

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“…• Data about the student • Student performance • Student self-assessment [24], [54], [61] Data accumulated in learning platforms…”

“…In addition, the student performance for a given discipline can be improved by including additional characteristics in the learning process that represent their previous courses results. [61] Data mining is a promising research area in education, extracting useful information from students without any prior hypotheses.…”

Perfiles de pensamiento docente de los estudiantes de ciencia y tecnología del máster de enseñanza secundaria

“…El marco formativo en el que se desarrolla este proyecto se sustenta en la perspectiva constructivista sobre la formación docente, en la que se concede gran importancia a la visión personal del profesorado sobre los procesos educativos (Chen & Bonner, 2017;Kingir et al, 2020). En esta línea de trabajo se han desarrollado numerosos estudios, en los que se aprecia que los profesores y profesoras en formación muestran ideas personales sobre la docencia que influyen en la práctica educativa posterior (Fuentes et al, 2009;Hernández y Maquilón 2010).…”

As estratégias de aprendizagem na formação inicial de docentes: uma revisão sistemática da literatura

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