DL performance comprises a general dimension plus four tool-specific residual dimensions, with the specific dimensions defined by the software applications used¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2–r3 · q2
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A bifactor model with a global dimension and four specific dimensions fit better than a simple four-dimensional model without a general dimension. → Frank Reichert 2020 q2 i? The specific residual dimensions capture commonality due to students' familiarity with the assessment tools and contexts rather than mapping onto existing DL frameworks. → Frank Reichert 2020 (2)

Evidence¶

Frank Reichert 2020¶

Frank Reichert, Nancy W. Y. Law, Gary K. W. Wong, Jimmy de la Torre. (2020). Exploring the structure of digital literacy competence assessed with authentic software applications. Education Tech Research Dev, 68, 2991–3013. https://doi.org/10.1007/s11423-020-09825-x

q2 · i? · associational · r2

Confirmatory bifactor analysis of thirteen ILPA items in Results: the model combining a general dimension with four specific dimensions fit acceptably (RMSEA = .052, CFI = .978) and "performed better than a simple four-dimensional model without a general dimension".

"The bifactor model had an acceptable to good model fit (RMSEA = .052, CFI = .978, SABIC = 15,963) and performed better than a simple four-dimensional model without a general dimension (RMSEA = .096, CFI = .909, SABIC = 16,334)."

Frank Reichert 2020 (2)¶

Frank Reichert, Nancy W. Y. Law, Gary K. W. Wong, Jimmy de la Torre. (2020). Exploring the structure of digital literacy competence assessed with authentic software applications. Education Tech Research Dev, 68, 2991–3013. https://doi.org/10.1007/s11423-020-09825-x

q2 · i? · associational · r3

Authors' interpretation in the Discussion of the bifactor results: the specific "residual" dimensions "seem to be defined by the software applications that students used in the assessment", reflecting tool and context familiarity.

"Instead, those dimensions seem to be defined by the software applications that students used in the assessment; they capture commonality among students' performance that seems to be due to their familiarity with the assessment tools and/or the context (RQ2)."

Discussion¶

Related Claims¶

• Technical digital literacy measured with authentic software applications is not a unidimensional construct; a four-dimensional exploratory MIRT model fits best — related
• Bifactor-ESEM shows the highest fit among CFA, ESEM, bifactor-CFA and bifactor-ESEM models, supporting AITAQS as one general factor plus four specific dimensions. — related
• The same bifactor DL structure holds for primary and secondary students under partial measurement invariance, permitting comparison of performance levels across cohorts — related
• Secondary students outperformed primary students on general DL and online information seeking, but primary students performed slightly better on presentation-related DL — related
• Only about 62% of the reliable variance in total DL scores is attributable to the general dimension, supporting a multidimensional interpretation — related