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This study investigates the factors influencing the integration of information literacy into teaching practices among elementary school teachers in Taiwan, particularly following the COVID-19 pandemic and the introduction of new national curriculum guidelines. Utilizing Hierarchical Linear Modeling (HLM), this research examines the effects of both teacher-level and school-level factors on the integration process. A sample of 1,766 teachers from 117 elementary schools was analyzed through a multistage cluster sampling design. In addition, qualitative interviews provided deeper insights into teachers’ perspectives. The results reveal that administrative support, workload arrangements, professional development opportunities, and government policies at the school level significantly impact the integration of information literacy. At the teacher level, factors such as gender, experience, educational background, and attitude play crucial roles. The study’s findings offer valuable recommendations for policymakers and school administrators to enhance information literacy integration in educational settings.

Automated Writing Evaluation (AWE) provides automatic writing feedback and scoring to support student writing and revising. The purpose of the present study was to analyze a statewide implementation of an AWE software (n=114,582) in grades 4-11. The goals of the study were to evaluate (a) to what extent AWE features were used, (b) if equity and access issues influenced AWE usage, and (c) if AWE usage was associated with writing performance on a large-scale state writing assessment. Descriptive statistics and hierarchical linear modeling were used to answer the research questions. Results indicated that the main feature of AWE (i.e., writing and revising essays) were used but some features (peer review and independent lessons) were underutilized. School and student level demographic variables explained little variance in AWE usage. AWE usage was statistically and positively associated with performance on a large-scale state writing assessment when controlling for prior performance and demographics. The study presents evidence that AWE can positively influence writing on a distal measure when implemented at-scale. Implications for large-scale AWE implementation are discussed.

This study aimed to explore and compare individual student-level and teacher-level teacher expectation effects on student academic achievement in the Chinese junior high school context. The participants were 50 teachers and their 1199 students from 10 junior high schools. With differences in student baseline achievement controlled, hierarchical linear modelling was employed to see if early-year teacher expectations predicted student year-end achievement. Results showed that both student- and teacher-level expectations (relative to achievement) positively predicted student academic achievement. Teacher expectations at the student level showed a stronger influence on student-achievement outcomes. The results also indicated that teachers tended to hold higher expectations for girls than for boys and were more likely to underestimate students who were children of migrant workers.

Hierarchical linear modeling (HLM) has become increasingly popular in the higher education literature, but there is significant variability in the current approaches to the conducting and reporting of HLM. The field currently lacks a general consensus around important issues such as the number of levels of analysis that are important to include and how much variance should be accounted for at each level in order for the HLM analysis to have practical significance (Dedrick et al., Rev Educ Res 79:69–102, 2009). The purpose of this research is to explore the use of a 3-level HLM model, appropriate contextualizing of results of HLM, and the interpretation of HLM results that resonates with practice. We used an example of a 3-level model from the National Study of Living Learning Programs to highlight the practical issues that arise in the interpretation of HLM within a higher education context.

The results of the Programme for International Student Assessment (PISA) 2012 showed that Indonesia, Malaysia, and Thailand underperformed and were positioned in the bottom third out of 65 participating countries for mathematics, science, and reading literacies. The wide gap between these three countries and the top performing countries has prompted this study to examine the influence of students' affective characteristics on their performance in mathematics literacy using a multilevel analysis. The purpose of this study is to examine the relationships among affective characteristics-related variables at the student level, the aggregated school-level variables, and mathematics performance by using the Programme for International Student Assessment (PISA) 2012 dataset. The data used for the analysis were retrieved from the official PISA website. The student samples from Indonesia, Malaysia and Thailand were 5, 622, 5, 192 and 6, 602, respectively. The data were analysed using descriptive statistics, and a hierarchical linear modeling (HLM) approach with the HLM version 7.0 computer programme. Different patterns of relationships were found between student- and school-level variables and mathematics performance in the three countries. The common student-level variable is attitudes towards learning outcomes, which predicted an increase in scores for the Indonesian, Malaysian, and Thai models. At the student level, the strongest predictor on mathematics literacy performance was mathematics self-efficacy for both Indonesian and Malaysian models, and perseverance for the Thai Model. At the school level, school average mathematics self-efficacy was the strongest predictor of mathematics performance in the Indonesian model; average openness to problem-solving in the Thai model; and school average instrumental motivation, mathematics behaviour, and attitudes towards learning outcomes predicted a decrease in scores for the Malaysian model. The inconclusive results of the multilevel analysis has demonstrated some interesting points for discussion, though the results could be attributed to the differences in education system and a diversity of cultural context in each individual country. This study contributes to providing evidence-based policy making in addition to informing the mathematics teachers the particular students' affective characteristics, which should be strengthened to ensure better mathematics learning outcomes in Indonesia, Malaysia, and Thailand. Implications of the findings and limitations are discussed. [Author abstract, ed]

Teacher engagement plays a crucial role in shaping student motivation at both individual and group levels. This study employed a Hierarchical Linear Modeling approach to analyze the dynamic relationship between teacher engagement and student motivation, considering the contributions of teaching experience and average teacher engagement at the group level. Data were collected from 112 students and seven teachers over four sessions, resulting in a total of 448 observations at Level 1 (session level) using questionnaires to measure teacher engagement and student motivation. Data analysis used hierarchical linear modeling with Mplus 7.0. The analysis revealed that teacher engagement significantly increased student motivation at the within level. At the between-person level, teacher engagement was found to play a meaningful role in enhancing student motivation. Additionally, teaching experience also contributed positively, indicating that more experienced teachers were generally more effective in fostering student motivation, while teaching experience demonstrated a significant contribution with p = .035. Additionally, a positive correlation was observed between teaching experience and teacher engagement at the group level. To build upon these results, future research is encouraged to adopt a long-term longitudinal design to understand stable patterns. Furthermore, more holistic measurement instruments, such as direct classroom observations and qualitative data, are recommended.

The Objective Structured Clinical Exam (OSCE) is a widely used method of assessment in medical education. Rater cognition has become an important area of inquiry in the medical education assessment literature generally, and in the OSCE literature specifically, because of concerns about potential compromises of validity. In this study, a novel approach to mixed methods that combined Ordinal Logistic Hierarchical Linear Modeling and cognitive interviews was used to gain insights about what examiners were thinking during an OSCE. This study is based on data from the 2010 to 2014 administrations of the Clinician Assessment for Practice Program OSCE for International Medical Graduates (IMGs) in Nova Scotia. An IMG is a physician trained outside of Canada who was a licensed practitioner in a different country. The quantitative data were examined alongside four follow-up cognitive interviews of examiners conducted after the 2014 administration. The quantitative results show that competencies of (1) Investigation and Management and (2) Counseling were highly predictive of the Overall Global score. These competencies were also described in the cognitive interviews as the most salient parts of OSCE. Examiners also found Communication Skills and Professional Behavior to be relevant but the quantitative results revealed these to be less predictive of the Overall Global score. The interviews also reveal that there is a tacit sequence by which IMGs are expected to proceed in an OSCE, starting with more basic competencies such as History Taking and building up to Investigation Management and Counseling. The combined results confirm that a hidden pattern exists with respect to how examiners rate candidates. This study has potential implications for research into rater cognition, and the design and scoring of practice-ready OSCEs.

This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the calculation and interpretation of the effect size measures using a simulated dataset and an empirical dataset from a cluster-randomized trial of peer tutoring. SAS syntax is provided for parametric percentile bootstrapped confidence intervals of the effect sizes. The use of any of the 3 standardized effect size measures depends on the nature of the inference the researcher wishes to make within a single site, across the broad population, or at the site level.

As the COVID-19 pandemic upended the 2019–2020 school year, education systems scrambled to meet the needs of students and families with little available data on how school closures may impact learning. In this study, we produced a series of projections of COVID-19-related learning loss based on (a) estimates from absenteeism literature and (b) analyses of summer learning patterns of 5 million students. Under our projections, returning students are expected to start fall 2020 with approximately 63 to 68% of the learning gains in reading and 37 to 50% of the learning gains in mathematics relative to a typical school year. However, we project that losing ground during the school closures was not universal, with the top third of students potentially making gains in reading.

This study examines the prevalence, contexts, and demographic correlates of monotonic response patterns (MRPs) in online student evaluations. Results of two-level hierarchical generalized linear models show evidence of careless monotonic responses to a survey administered to students enrolled in a university-level foreign language course in the Republic of Korea. All else being equal, freshmen and students in classes with fewer survey participants were more likely to choose monotonic response patterns in course evaluations. Possible factors at work in generating MRPs are identified and discussed. The severity of the MRP problem in online ratings underscores the importance for administrators to consider possible validity threats in student evaluations before using them as tools to inform instructional and administrative decisions. It is also important to design course evaluation surveys in such a way as to minimize careless responses and to identify means to induce more thoughtful responses from college students. (HRK / Abstract übernommen).