Teachers' perceived utility-value of technology is associated with how often and how well they integrate it; self-efficacy showed no significant association with frequency, exploitation or task-specific quality¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2021 · checked against its sources 2026-10-30 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? Utility-value was significantly related to frequency of technology integration, technology exploitation, and task-specific teaching quality, while self-efficacy was not related to any of these outcomes. → Backfisch 2021

Evidence¶

Backfisch 2021¶

Backfisch, I., Lachner, A., Stürmer, K., & Scheiter, K. (2021). Variability of teachers’ technology integration in the classroom: A matter of utility!. Computers & Education, 166, 104159. https://doi.org/10.1016/j.compedu.2021.104159

q2 · i? · associational · r2

Linear mixed effect models on diary data from 18 teachers found utility-value was related to the frequency of technology integration, technology exploitation and task-specific teaching quality, while self-efficacy showed no significant relation to any of these. Both utility-value and self-efficacy were related to task-general teaching quality (smoothness of integration; self-efficacy Estimate = 0.448, 95% CI [0.124, 0.771]). The authors note the correlational design cannot say whether utility-value is a cause or a consequence of integration. The article reports "utility-value was related to technology-exploitation" with the printed estimates and confidence intervals excluding zero.

"utility-value was related to technology-exploitation, Estimate = 0.367, SE = 0.325, 95% CI [0.161, 0.573], and task-specific teaching quality, Estimate = 0.189, SE = 0.070, 95% CI [0.050, 0.327]."

Discussion¶

Related Claims¶

• Both self-efficacy and utility-value predict task-general teaching quality (smoothness) in technology-based lessons — reports the opposite
• Teachers' expectancy beliefs about successfully using AI applications shape their motivation to use them (theoretical argument). — related
• Learners invest more effort in tasks they perceive as interesting, personally important, or useful to their future goals. — a broader claim this one bears on
• Teachers who believe AI will help them reach professional goals and improve students' learning outcomes (utility value) may be more motivated to use it (theoretical argument). — related