In search of a measure to address different sources of cognitive load in computer-based learning environments

DÖNMEZ O., AKBULUT Y., TELLİ E., Kaptan M., Ozdemir I. H., ERDEM M.

EDUCATION AND INFORMATION TECHNOLOGIES, vol.27, no.7, pp.10013-10034, 2022 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 7
  • Publication Date: 2022
  • Doi Number: 10.1007/s10639-022-11035-2
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Communication Abstracts, EBSCO Education Source, Educational research abstracts (ERA), ERIC (Education Resources Information Center), INSPEC
  • Page Numbers: pp.10013-10034
  • Keywords: Cognitive load, Human computer interface, Scale development, Exploratory factor analysis, Confirmatory factor analysis, EXPLORATORY FACTOR-ANALYSIS, INSTRUCTIONAL-DESIGN, RECONSIDERATION, RECOMMENDATIONS, ARCHITECTURE, FUTURE
  • Anadolu University Affiliated: Yes


In the current study, we aimed to develop a reliable and valid scale to address individual cognitive load types. Existing scale development studies involved limited number of items without adequate convergent, discriminant and criterion validity checks. Through a multistep correlational study, we proposed a three-factor scale with 13 items to address intrinsic, extraneous and germane cognitive load in computer-based learning environments. A thorough literature search of cognitive load indicators in the literature was followed by expert panels for content and construct validity. The initial item pool was administered to 236 undergraduate students who watched an instructional video on IP address classes. A multiple-choice achievement test was also implemented after the intervention. The exploratory factor analysis with maximum likelihood explained 58 percent of the variance with factor loads above .49, and internal consistency coefficients above .81. Convergent and discriminant validity indices were acceptable. Besides, achievement was related positively with the germane load and negatively with intrinsic and extraneous load. Fifteen percent of the achievement was explained by the three sources of the cognitive load. Then, the developed factor structure was validated with 193 undergraduate students immediately after they participated in online webinars, and with 99 undergraduate students after they participated in face to face classes. The proposed structure was confirmed in both settings so the scale was considered a reliable and valid indicator of cognitive load in both online and face-to-face learning environments.