Exploring K-12 Educators’ Types of Support Usage During Game Play

• In Event: Poster Session 10
  In Poster Session: PS 10 Topic - Education, Schooling

Abstract

Numerous studies suggest that instructional support (e.g., feedback, scaffolding) can significantly improve learning outcomes for game-based learning (Wouters & van Oostendorp; 2013). However, it is often undermined by a lack of clarity from an instructional perspective in terms of how teachers should integrate games into their teaching to support student learning (Girard et al., 2013). Previously developed self-reporting instruments on games have often focused on assessing the role of teacher factors (e.g., gender, teaching experience) and affective factors (e.g., attitudes, beliefs, confidence, motivation) on their behaviors or intention to integrate games into classrooms (Hsu et al., 2017; 2020). While such measures provide essential information about successful integration of games, it has not explicitly identified how teachers’ support the use of educational games as learning tools. Thus, this proposed study will develop and test a measure of teacher game-based learning supports during gameplay.

The methodology of developing and validating the items in the measure included three steps: First, identification of theoretical models for the measure was performed via a systematic literature review. Second, types of teacher supports were identified, and construct definitions written, largely based on Wood et al.’s (1976) work on scaffolding and studies conducted in the context of technology used classrooms (e.g., Yelland & Masters; 2007; Sharma & Hannafin 2007; Kim & Hannafin 2011). Second. initial items for each support were created by the first author and then checked for face validity by the second author via comparing items to construct definitions. Third, feedback on item clarity was sought from three educators. The resulting measure includes 20 Likert-scale items assessing six different types of support. Operational definitions and examples for each support type are found in Table 1. Fourth, the final step is to validate the measure with educators.

The measure will be hosted on the online survey recruitment and administration websites Prolific and Qualtrics, respectively. This study plans to recruit 100 English speaking adults employed currently as K-12 educators, of any subject and grade, who live in North America. A priori power analysis indicated that 100 participants is needed to detect large effects (d =0.8), with 90% power at an alpha of 0.05 (Kyriazos 2018; Soper, 2020). Educators will provide basic demographic information and complete the teacher support measure. To establish structural validity and an estimation of the internal consistency of the items, Confirmatory Factor Analysis (CFA) will be used, and items will be removed or adjusted based on the results (Meyers, Gamst, & Guarino, 2016, pp 493-522). This validation study will be conducted in November to December of 2020. Once validated, the updated version of the measure will be used in future studies of teacher supports during game-based learning and made freely available.

Findings from this study will advance knowledge on game-based learning by examining teacher supports that is largely understudied and will contribute to methodology by creating the first game-based learning teacher support measure.

Authors

• Gulsah Kacmaz, McGill University
  Presenting Author

• Adam Kenneth Dubé, McGill University
  Non-Presenting Author