Previous research has extensively analysed teachers' and students' Facebook use for instructional engagement, writing, research dissemination and e-learning. However, Facebook as a data collection mechanism for research has scarcely been the subject of previous studies. The current study addressed these gaps by analysing students' awareness, willingness, and utilisation of Facebook for research data collection [RDC]. This study aimed to predict students’ Facebook use for research data collection based on their awareness and willingness and to determine age and gender differences in (...) such predictions. A sample of 11,562 students of tertiary institutions participated in an online survey. The researchers designed and validated the online survey. Exploratory Factor Analysis was used for dimensionality. Average Variance Extracted [AVE] was used for convergent validity, whilst the Fornel-Larcker criterion and Heterotrait-Monotrait ratio were used for discriminant validity. Composite reliability indices of .97, .94 and .90 proved the instrument’s three sub-scales usable. One sample t-test and multigroup analysis were conducted using SPSS 27 and Smart PLS 3. The study found high awareness but low usage of Facebook for RDC among Nigerian university students. Younger and male students showed higher willingness and actual use of Facebook for RDC. Awareness directly impacted by awareness, and acted as Facebook usage for RDC, with stronger impacts for males and students aged 19 to 28. Willingness was positively impacted and acted as a negative mediator between awareness and usage for some age groups and genders. These findings provide important insights into using Facebook for RDC among students in Nigerian universities and highlight the need to consider the impact of demographic factors such as age and. (shrink)

Education is a necessary tool for national development and for development to take place, it is important that teachers who are the driving force of passing instructions to students in the school must be properly mentored for them to carry out their assignments. It is on the basis of this background that this study was conducted. Descriptive survey research design was found suitable for the study. A multistage sampling technique was employed in this research and 100 respondents were selected for (...) the study. Four research questions and hypotheses were raised and formulated and a questionnaire titled mentoring strategies and school effectiveness was developed for data gathering. The instument was validated and the reliability co-efficient of .84 was achieved. The finding revealed that there was significant relationship between mentoring strategies and secondary school effectiveness in Ilorin East, Kwara State. It was recommended among other things that teachers should be trained using mentoring strategies and that school administrators should ensure activities which will involve pairing new with experienced teachers so that they will gain new experience from the models. (shrink)

Background: Previous studies assessing students’ learning outcomes and identifying contributing factors have often dwelt on the cognitive domain. Furthermore, school evaluation decisions are often made using scores from cognitive-based tests to rank students. This practice often skews evaluation results, given that education aims to improve the three learning domains. This study addresses this gap by assessing the contributions of four students’ input to their cognitive, affective and psychomotor skills (CAPs). Methods: A cross-section of senior secondary class II students (n = (...) 870), sampled through the multistage procedure, participated in a physical survey. Students’ Inputs Questionnaire (STIQ) and Learning Outcomes Questionnaire (LOQ) were used for data collection. Based on data obtained from a pilot sample (n = 412), principal axis factoring (PAF) was performed to assess the internal structure of the instruments following an oblique rotation. The KMO value of sampling adequacy were 0.88 and 0.94, while the Bartlett’s test of sphericity were significant χ2 (253) = 5,010; p < 0.001 and χ2 (105) = 3693.38, p < 0.001 for the STIQ and LOQ, respectively. Confirmatory factor analysis was used to assess the models’ acceptability based on the maximum likelihood estimation technique. The main study used hierarchical linear regression for data analysis. Results: Findings indicated that innate ability, health, motivation and social capital relatively and cumulatively predicted students’ overall, cognitive, affective and psychomotor learning outcomes. The proportion of variance explained by the predictors increased at different levels of the models with the addition of new variables. Students’ social capital reduced the effect of students’ innate ability regardless of their motivation and health status. Conclusion/implication: This study has provided evidence that the four students’ inputs are crucial predictors of their learning outcomes in the three domains. This result is helpful for school management to provide services aimed at improving the school climate for students’ motivation and social capital. The result can provide policymakers with a proper understanding of the constituents of learning outcomes and how policies can be aligned to secure quality student inputs for maximum productivity in education. (shrink)