Gender predicted SRL profile membership, with male students more likely in lower SRL profiles; major, prior experience, and time commitment did not predict membership¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q3 · i0–i2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i2 Male students were more likely than female students to be in the minimal regulators, low regulators with limited social skills, and low regulators profiles than in the competent regulators profile. → Chungsoo Na 2024 q3 i0 Major, prior online learning experience, and time commitment showed no significant effects on SRL profile membership. → Chungsoo Na 2024 (2)

Evidence¶

Chungsoo Na 2024¶

Chungsoo Na, Soojeong Jeong, Jody Clarke-Midura, Youngin Shin. (2024). Linking self-regulated learning to community of inquiry in online undergraduate courses: A person-centered approach. Education Tech Research Dev. https://doi.org/10.1007/s11423-024-10380-y

q3 · i2 · associational · r2

Multinomial logistic regression with competent regulators as reference group, predicting profile membership from gender, major, prior experience, and time commitment. Male gender significantly predicted membership in three lower profiles, e.g. "OR = 2.36, 95% CI [1.14, 4.86]" for minimal vs. competent regulators.

"male students were more likely to be in the minimal regulators profile (OR = 2.36, 95% CI [1.14, 4.86], p = 0.020 in Profile 1 vs. Profile 5)"

Chungsoo Na 2024 (2)¶

Chungsoo Na, Soojeong Jeong, Jody Clarke-Midura, Youngin Shin. (2024). Linking self-regulated learning to community of inquiry in online undergraduate courses: A person-centered approach. Education Tech Research Dev. https://doi.org/10.1007/s11423-024-10380-y

q3 · i0 · associational · r2

Same multinomial logistic regression analysis. The three non-gender predictors were non-significant; the authors attribute this partly to a sample homogeneous in major and prior experience, and to reliance on self-report rather than LMS activity logs.

"We did not find any significant effects of the other three individual difference variables (i.e., major, prior experience, and time commitment) on determining SRL profile membership."

Discussion¶

Related Claims¶

• Latent profile analysis of 750 online undergraduates identifies five distinct SRL profiles, including a qualitatively distinct low-regulators-with-limited-social-skills profile — related
• Greater prior AI learning experience and female gender predicted upward motivational profile transitions — related
• Higher self-rated Japanese proficiency, more frequent AI use, and prior AI-related training predicted membership in the positive versus maladaptive adaptation profile at Time 1 — related
• Female gender, higher autonomous motivation, lower prior knowledge, and higher final course grades each predict more frequent voluntary StatBot use among graduate statistics students — related
• Self-reported SRL strategies vary by individual learner characteristics including demographics, prior experience, and motivations — related
• The low regulators with limited social skills profile showed significantly lower social presence than the low regulators profile, with no significant difference in teaching or cognitive presence — related