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ANCOVA post-test on experimental (STAD) and control (ICI) groups 

ANCOVA post-test on experimental (STAD) and control (ICI) groups 

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This study investigated the effectiveness of computer-assisted Students’ Team Achievement Division (STAD) cooperative learning strategy on physics problem solving on students’ achievement and retention. It also examined if the performance of the students would vary with gender. Purposive sampling technique was used to select two senior secondary sc...

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... The issue is believed can be overcome by using students' worksheet and STAD cooperative learning method as some conducted studies reveal that students taught by using STAD of cooperative learning method and computer gain better performance in their Physics class than those taught with other methods 19 . The involvement of the students in learning process can be obtained through integrated activities and the students worksheet 20 . ...
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Executive summary/Abstract Adaptive teaching and individualization for k-12 students improves academic achievement: A meta-analysis of classroom studies Background The question of how to best deliver instruction to k-12 students has dominated the educational conversation, both in terms of theory and practice, since before 1960. Two predominant models have clashed: 1) traditional teacher-directed instruction (referred to here as teacher-centered T-C instruction), where there is little methodological adaptation for individual differences in ability, skills, interests, etc. among students; and 2) so-called student-centered instruction (referred to here as S-C instruction), deriving much of its theoretical justification and methodological intricacies from constructivist thought embodied in the works of Jean Piaget, Lev Vygotsky, Jerome Burner, and many others. While radical constructivism has never become dominant in k-12 schooling (except in a relatively small number of demonstration schools), there has been considerable interest in embedding some of the principles of constructivism into k-12 schooling. This is often referred to as individualized or adaptive instruction, meaning an operational concern for individual students, their abilities, interests, etc., which is nearly the opposite of T-C instruction. A great deal of research has demonstrated that approaches to individualism, such as mastery learning, collaborative and cooperative learning, problem-based learning, peer tutoring, and computer-based instruction, are effective in promoting achievement and attitudinal gains, as contrasted with T-C instruction, where mastery of content or subject matter is of the greatest concern, and the teacher is the ‘delivery mechanism.’ More recently, this has been extended to include video-based lectures often delivered through the internet, as proposed by proponents of blended learning and its variant the flipped classroom (e.g., Baepler, Walker, & Driessen (2014). Research has also demonstrated that T-C instruction is particularly useful in developing basic skills in areas such as reading, spelling, and math, (Stockard, Wood, Coughlin & Khoury, 2018). More recent theory and practice concerning T-C and S-C instruction suggests that neither perspective is entirely sufficient and that some combination of T-C and S-C instruction is possibly more productive. This notion of combined teaching methods (i.e., T-C plus S-C) is one of the defining characteristics of the flipped classroom (Baepler et al., 2014). Certainly, students need to acquire skills and knowledge, but they also need to develop their own personal preferences, creativity, problem-solving abilities, and evaluative and self-evaluative perspectives. The current meta-analysis aims to determine if the advantage endowed by S-C instruction also affects to content achievement (i.e., content achievement is the outcome measure in this meta-analysis). The current meta-analysis was designed to explore teaching and learning in k-12 classrooms and the achievement benefit that derives from more S-C versus less S-C classrooms. Several perspectives informed the basis for the research approach described here, but none more so than the words of Gersten et al. (2008) while exploring through meta-analysis the question of T-C versus S-C instructional practices in elementary mathematics instruction. In the final report of their study, the group stated: “The Task Group found no examples of studies in which learners were teaching themselves or each other without any teacher guidance; nor did the Task Group find studies in which teachers conveyed … content directly to learners without any attention to their understanding or response. The fact that these terms, in practice, are neither clearly nor uniformly defined, nor are they true opposites, complicates the challenge of providing a review and synthesis of the literature …” (p. 12). The current meta-analysis intends to investigate variations of more versus less S-C instruction and the four domains of the instructional process in which they are more or less profitable. Objectives (research questions) There are three primary objectives that this meta-analysis intends to address (research questions that this study explores): • Overall, does more S-C instructional practices lead to a significant advantage in the acquisition of content (subject matter) knowledge (i.e., measured learning achievement)? • Do any of the four primary (substantive) moderator variables (entered into multiple meta-regression), Teacher’s Role, Pacing, Adaptability, and Flexibility, predict an increase or decrease in achievement across degrees of S-C use (From less S-C to more S-C)? • Is there a difference in categorical levels of less S-C to more S-C for each of the dimensions of instructional practice listed above, tested in mixed moderator variable analysis? • Do any of the secondary (demographic) moderator variables interact with each other (i.e., combine) to produce more versus less S-C instructional practices? Search methods Following the guidelines of the Campbell Collaboration (Kugley et al., 2017), in order to retrieve a broad base of studies to review, we started by having an experienced Information Specialist search across an array of bibliographic databases, both in the subject area and in related disciplines. The following databases were searched for relevant publications: ABI/Inform Global (ProQuest), Academic Search Complete (EBSCO), ERIC (EBSCO), PsycINFO (EBSCO), CBCA Education (ProQuest), Education Source (EBSCO), Web of Knowledge, Engineering Village, Francis, ProQuest Dissertations & Theses Global, ProQuest Education Database, Linguistics and Language Behavior Abstracts (ProQuest). The search strategy was tailored to the features of each database, making use of database-specific controlled vocabulary and search filters, but based on the same core key terms. Searches were limited to the year 2000 to 2017, and targeted a k-12 population. Database searching was supplemented by using the Google search engine to locate additional articles, but principally grey literature (research reports, conference papers, theses and research published outside conventional journals). Selection criteria The overall set of inclusion/exclusion criteria (i.e., selection) for the meta-analysis contained the following requirements: • Be publicly available (or archived) and encompass studies from 2000 to the present; • Feature at least two groups of different instructional strategies/practices that can be compared according to the research question as S-C and T-C instruction; • Include course content and outcome measures that are compatible in the groups that form these comparisons; • Contain sufficient descriptions of major instructional events in both instructional conditions; • Satisfy the requirements of either experimental or high-quality quasi-experimental design; • Be conducted in formal k-12 educational settings eventually leading to a certificate, diploma, degree, or promotion to a higher grade level; • Contain legitimate measures of academic achievement (i.e., teacher/researcher-made, standardized); and • Contain sufficient statistical information for effect size extraction. Data collection and analysis Effect size extraction and calculation One of the selection criteria was “Contain sufficient statistical information for effect size extraction,” so that an effect size could be calculated for each independent comparison. This information could take several forms (in all cases sample size data were required): • Means and standard deviations for each treatment and control group; • Exact t-value, F-value, with an indication of the ± direction of the effect; • Exact p-value (e.g., p = .011), with an indication of the ± direction of the effect; • Effect sizes converted from correlations or log odds ratios; • Estimates of the mean difference (e.g., adjusted means, regression β weight, gain score means when r is unknown) • Estimates of the pooled standard deviation (e.g., gain score standard deviation, one-way ANOVA with three or more groups, ANCOVA); • Estimates based on a probability of a significant t-test using α (e.g., p < .05); and • Approximations based on dichotomous data (e.g., percentages of students who succeeded or failed the course requirements). Effect sizes were initially calculated as Cohen’s (Cohen, 1988) and then converted to Hedges’ (i.e., correction for small samples; Hedges & Olkin, 1985). Standard errors ( ) were calculated for and then converted to standard errors of applying the correction formula for . Hedges’ , and sample sizes (i.e., treatment and control) were entered into Comprehensive Meta-Analysis 3.3.07 (Borenstein, Hedges, Higgins, & Rothstein, 2014) where statistical analyses were performed. The effect sizes were coded for precision and these data were analyzed in moderator variable analysis. Statistical analyses Analyses were conducted using the following statistical tests: • Overall weighted random effects analysis with the statistics of , , , upper and lower limits of the 95th confidence interval, , and p-value; • Homogeneity is estimated using Q-Total, df, and p-value. I2 (i.e., percentage of error variation) and tau2 (i.e., average heterogeneity) is also calculated and reported. • Meta-regression (single and multiple) is used to determine the relationship between covariates and effect sizes; and • Mixed-model (i.e., random and fixed) moderator variable analysis is used to compare levels (categories) of each coded moderator variable. Q-Between, df, and p-value are used to make decisions about the significance of each categorical variable. Results The results are presented here in relationship to the four research questions previously described. • Question 1: Overall, does more S-C instructional practices lead to a significant advantage in the acquisition of content (subject matter) achievement (i.e., measured learning). • Result: Answering the basic question, more S-C instructional conditions (i.e., the treatment described above) outperform less S-C to a moderate extent. The average effect, = 0.44, k = 365, z =4.56, p < .00, SE = 0.03, Q = 3,095.89, I2 =88.22, tau2 = 0.27, between the mean of the > S-C treatment and the < S-C control, suggesting that teachers who promote and enact active, student-based classroom processes (more S-C instruction), can expect to see better student achievement than in classrooms where teachers employ less-student-based (less S-C) instruction. Also, a linear trend was found in meta-regression when Hedges’ was regressed on degree of S-C instruction ( = 0.04, SE = 0.02, z = 2.41, p = .032). The distribution remains significantly heterogeneous. • Question 2: Do any of the four moderator variables (entered into multiple meta-regression), Teacher’s Role, Pacing, Adaptability, and Flexibility, predict an increase or decrease in achievement across degrees of S-C use (From less S-C to more S-C)? Result: In meta-regression, Teacher’s role produces a significant linear trend ( = 0.06, SE = 0.04, z = 4.42, p < .001) and Pacing ( = -0.14, SE = 0.04, z = 3.18, p = .002). Adaptability, and Flexibility are not significant (p > .05). However, the trend for Teacher’s role and Pacing is opposite (note the opposite signs on ). Teacher’s role is significantly positive (i.e., more S-C instruction produced higher achievement), while Pacing produces the reverse (i.e., a significantly negative trend). For Pacing, more S-C methods produce lower achievement. Question 3: Do any of the moderator variables interact with each other (i.e., combine) to produce more versus less S-C instructional practices? • Result: Yes, Teacher’s Role compared to two dimensions added to the Teacher’s Role produce significantly different results (Q-Between = 7.76, df = 3, p = .02: Teacher’s Role and Teacher’s Role plus Adaptability significantly outperformed Teacher’s Role plus Flexibility. • Question 4: Is there a difference in categorical levels of less S-C to more S-C for each of the dimensions of instructional practice listed above, tested in mixed moderator variable analysis? Result: Only one of five moderator variables produced a significant differentiation among levels. Among four moderator variables (i.e., grade level; STEM vs. Non-STEM subjects; individual subjects; and ability profile) only ability profile significantly differentiated among levels. Special education students demonstrated significantly higher achievement compared to the General population of students. Authors’ conclusions This meta-analysis provides strong evidence that S-C instruction leads to improvements in learning with k-12 students. Not only is the overall random effects average effect size of medium strength ( = 0.44), but there is also a demonstrated (subtle but significant) linear relationship between more S-C classroom instruction and effect size (p = .03). Taken together, these results support the efficacy of allowing students to engage in active learning or other forms of S-C as part of a comprehensive educational experience