The purpose of this study was to examine the underlying mechanism between goal orientations and academic expectation for online learners. We simultaneously studied the structural relationships among 2×2 achievement goal orientations, self-regulated learning (SRL) strategies, supportive online learning behaviors, and expected academic outcome in various online courses with 93 respondents (70 undergraduate and 23 graduate students). Specifically, we tested the mediation effects of both SRL strategies and supportive online learning behaviors on the relationship between achievement goal orientations and students' academic expectations. The results showed that two of the achievement goal orientations-mastery-approach (MAP) goals and mastery-avoidance (MAV) goals-predicted the adoption of SRL strategies and supportive online learning behaviors, which, in turn, predicted students' expected academic outcome for their online course. Specifically, students with higher MAP goals were more likely to adopt different types of SRL strategies and supportive online learning behaviors to facilitate their learning experience, which further enhanced their expectation for their academic outcome. By contrast, students with higher MAV goals were less likely to adopt SRL strategies and supportive online learning behaviors, which, in turn, led to lower grade expectations. . (2019).How college students' achievement goal orientations predict their expected online learning outcome: The mediation roles of self-regulated learning strategies and supportive online learning behaviors. Online Learning, 23(4), 23-41.

Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results. Ten years have passed since the guidelines for reporting multilevel studies were initially published. This study reviewed new advancements in MLM and revisited the reporting practice in MLM in the past decade. A total of 301 articles from 19 journals representing different subdisciplines in education and psychology were included in the systematic review. The results showed improvement in some areas of the reporting practices, such as the number of models tested, centering of predictors, missing data treatment, software, and estimates of variance components. However, poor practices persist in terms of model specification, description of a missing mechanism, power analysis, assumption checking, model comparisons, and effect sizes. Updates on the guidelines for reporting multilevel studies and recommendations for future methodological research in MLM are presented.

Recently, researchers have used multilevel models for assessing intervention effects in singlecase studies, which are based on the replication of interrupted time-series designs across a small number of cases. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated single-case data. Simulated conditions varied with regard to the number of participants, series length, and the distribution of the variance within and across cases. REML and Bayesian estimation led to unbiased estimates of the fixed effects, but differentially impacted the inferences about the fixed effects and the estimates of the variances.Implications for applied single-case researchers and methodologists are discussed.