Raw Data Developed Model for Factors Affecting Indonesian PSTs’ Technology Integration during teaching practices
This dataset's raw data describes the variables that may affect the preservice teachers (PSTs) technology integration during teaching practices. This dataset addressed seven main aspects of technology integration, including demographic profile information, PSTs’ teaching preparation, acceptance and intention to use technology, PSTs' teacher technology skills, level of innovativeness, PSTs' technology integration during teaching practices, and the quality of PSTs’ technology integration.
Steps to reproduce
We first referred to the systematic literature review (SLR) to develop the model in this dataset. This SLR adopted the Reporting Items Systematic Reviews and Meta-Analysis (PRISMA) 2020 protocol , which reviewed the studies related to preservice technology integration during teaching practice in the last six years (2017–2022). From the SLR, we found that almost half of the studies focused on TPACK. The reviewed studies still consider future studies using the TPACK framework and modifying it with other theories or frameworks to facilitate the PSTs’ preparation program. However, by incorporating and proposing the new models, the reviewed studies underlined that future studies must be careful to measure the new model and ensure that model is validated. One of the other key findings of this SLR is that the studies chosen only focus on six aspects: ICT competencies, readiness, teaching preparation, and environmental support and belief in integrating technology into teaching. Regarding the results of the reviewed study, as mentioned earlier in this section, almost half of the reviewed studies only focus on how the PSTs implemented the technology in their teaching practice and TPACK. Only two studies combined more than two theories or frameworks , . To deal with these challenges, we developed this model. In this dataset, the TPACK framework  was introduced as an aspect of PSTs’ teaching preparation (TPP). Moreover, indicators of the PST’s acceptance and intention to use technology (AIT) during teaching practice were generated from the unified theory of acceptance and use of technology (UTAUT) . This dataset used the Sailer et al. (2021) teacher technology skill (TTS) model as a direct determinant to evaluate how PSTs integrated technology with learning activities for effective and efficient sequences. To identify the level of PST’s innovativeness (LOI) in adopting and integrating technology in teaching and practice, this dataset adapted Rogers’s diffusion and innovation theory , . In this dataset, Roger’s DOI will be used as a factor to explain which factors influence an individual's decision to accept or reject technology innovation during teaching practice. Additionally, to investigate in what ways the PSTs engage with their students during teaching and learning, this dataset also examines the quality of the PSTs' technology integration (QTI). To measure the quality of PST’s technology integration, we combined the technology integration quality instrument  and the Triple E evaluation rubric , .