The effects of within-class grouping on student achievement and other outcomes were quantitatively integrated using two sets of study findings. The first set included 145 effect sizes and explored the effects of grouping versus no grouping on several outcomes. Overall, the average achievement effect size was +0.17, favoring small-group learning. The second set included 20 effect sizes which directly compared the achievement effects of homogeneous versus heterogeneous ability grouping. Overall, the results favored homogeneous grouping; the average effect size was +0.12. The variability in both sets of study findings was heterogeneous, and the effects were explored further. To be maximally effective, within-class grouping practices require the adaptation of instruction methods and materials for small-group learning.

This study quantitatively synthesized the empirical research on the effects of social context (i.e., small group versus individual learning) when students learn using computer technology. In total, 486 independent findings were extracted from 122 studies involving 11,317 learners. The results indicate that, on average, small group learning had significantly more positive effects than individual learning on student individual achievement (mean ES = +0.15), group task performance (mean ES = +0.31), and several process and affective outcomes. However, findings on both individual achievement and group task performance were significantly heterogeneous. Through weighted least squares univariate and multiple regression analyses, we found that variability in each of the two cognitive outcomes could be accounted for by a few technology, task, grouping, and learner characteristics in the studies.Computer technology (CT) and the tremendous growth of information technologies are transforming the world and the way education is conducted. Electronic data processing, information systems, graphic designs, and computer-mediated communication are making the computer an increasingly indispensable tool in nearly every aspect of work and life. In schools, students are using CT to facilitate their learning in various subjects as well as to acquire CT knowledge and skills to meet the challenges in this rapidly changing technological and information age.

Many colleges and universities have adopted the use of student ratings of instruction as one (often the most influential) measure of instructional effectiveness. In this article, the authors present evidence that although effective instruction may be multidimensional, student ratings of instruction measure general instructional skill, which is a composite of three subskills: delivering instruction, facilitating interactions, and evaluating student learning. The authors subsequently report the results of a metaanalysis of the multisection validity studies that indicate that student ratings are moderately valid; however, administrative, instructor, and course characteristics influence student ratings of instruction. Characteristics of Effective InstructionItems on student rating forms reflect the characteristics that experts believe (a) can be judged accurately by students and (b) are important to teaching, Nevertheless, researchers define instructional effectiveness from a num-

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We argue that the multisection validation design is the strongest design for addressing the degree to which student ratings predict teacher-produced learning. Results of several dozen multisection validity studies appear inconsistent. Unfortunately, prior quantitative reviews did not answer questions about the diversity of findings. The authors explore sensitivity of the prior analyses to identify true explanatory characteristics, generalizability of the findings across dimensions of teaching, and adequacy of the analyses to identify potential explanatory characteristics. They conclude that prior analyses lack adequate statistical power, explanatory characteristics vary with the dimension of teaching being validated, and a host of other study features remain to be investigated. Those features are identified through nomological coding of 43 validity studies.