A pilot meta-analysis of computer-based scaffolding in STEM education.
Despite much primary research and review work about scaffolding (Kali & Linn, 2008), scaffolding meta-analyses remain an emergent line of inquiry. Meta-analyses have been published on specific types of scaffolding including dynamic assessment (Swanson & Lussier, 2001), scaffolding for students with learning disabilities (Swanson & Deshler, 2003), and scaffolding in multimedia instruction (Lin, Ching, Ke, & Dwyer, 2007). However, a more comprehensive examination of scaffolding is needed to inform researchers and designers of the most effective characteristics of and approaches to researching scaffolding. In this paper, we use meta-analysis to determine the influence of computer-based scaffolding characteristics, study and test score quality, and assessment level on cognitive outcomes in science, technology, engineering, and mathematics (STEM) education. This paper is significant in that it (a) provides evidence of the effectiveness of scaffolding, (b) can guide future research on scaffolding, and (c) provides for data-driven scaffolding design decisions.
As Vygotsky (1962) noted, "The only good kind of instruction is that which marches ahead of development and leads it" (p. 104). Scaffolding can facilitate such instruction by providing conceptual, procedural, strategic, and metacognitive support that bridges the gap between what students can do on their own and what they can do with the help of a more capable other (Hannafin, Land, & Oliver, 1999; Wood, Bruner, & Ross, 1976). As originally defined, scaffolding referred to dynamic support provided by a teacher or other more capable other that enabled children to solve problems (Wood et al., 1976). The emergence of personal computers has allowed computer-based scaffolding to be developed to supplement teacher scaffolding (Hannafin et al., 1999; Saye & Brush, 2002). Computer-based scaffolding (hereafter referred to as "scaffolding") can promote success in rich problem solving contexts that require students to go beyond filling out worksheets or listening to a teacher lecture.
Meta-analyses of specific scaffolding types (scaffolding for dynamic assessment, learning disabilities, and multimedia instruction) indicate that scaffolding-related interventions were associated with increased student learning (Lin et al., 2007; Swanson & Deshler, 2003; Swanson & Lussier, 2001). Dynamic assessment led to an average effect size of 0.96, and there was a similarly positive relationship between explicit practice and learning (Swanson & Lussier, 2001). Scaffolding in multimedia instruction led to an average effect size of 0.02 (Lin et al., 2007). Thus, a meta-analysis that covers a wider range of scaffolding types is warranted.
Scaffolding can (a) enlist interest in the target task (Wood et al., 1976), (b) maintain direction (Wood et al., 1976), (c) reduce complexity (Reiser, 2004), (d) highlight important problem features (Reiser, 2004), (e) help students manage frustration (Wood et al., 1976), (f) model expert processes (van de Pol, Volman, & Beishuizen, 2010), and (g) elicit articulation (Reiser, 2004). Scaffolding can do this through such strategies as making thinking visible (Kali & Linn, 2008). No scaffolding serves all these aims, and no literature compares such strategies to indicate which are most effective in which contexts.
Though scaffolding originally referred to support for children's problem solving abilities (Wood et al., 1976), recent scaffolding work also supports (a) other higher-order thinking skills (e.g., argumentation and evaluation), and (b) knowledge integration--the ability to "expand, revise, restructure, reconnect and reprioritize" scientific models (Linn, 2000, p. 783). The literature does not indicate which scaffolding type leads to stronger outcomes.
Scaffolding is not a stand-alone intervention. Rather, it is used along with instructional approaches (paired interventions) such as problem-based learning (PBL) (Saye & Brush, 2002), learning by design (Puntambekar & Kolodner, 2005), and case-based learning (Lajoie, Lavigne, Guerrera, & Munsie, 2001). No literature comprehensively compares the effectiveness of scaffolding when used with these different approaches.
Many debate whether scaffolding should be generic or context-specific. For example, one finding indicated that generic scaffolds promoted deeper reflection among middle school students than content-specific scaffolds (Davis, 2003), while another finding indicated that content-specific scaffolds were more effective when teachers provided one-to-one scaffolding related to a general argumentation framework (McNeill & Krajcik, 2009).
Many researchers argue that scaffolds must be faded as students gain skill to promote transfer of responsibility (Pea, 2004; Puntambekar & Hubscher, 2005). In one-to-one scaffolding, this is done through continual diagnosis of student performance (van de Pol et al., 2010). The only two ways this has been accomplished among computer-based scaffolds to support ill-structured problem solving are making the scaffolds disappear (a) on a fixed schedule (Li & Lim, 2008) or (b) when students indicate that they do not need the scaffolding anymore (Metcalf, 1999). It is not clear if such approaches lead to greater learning or transfer of responsibility.
Scaffolding and methodological quality
Methodological quality considerations include threats to internal validity and external validity (Gall, Gall, & Borg, 2003; Shadish & Myers, 2001), as well as test score reliability and validity (Messick, 1989). It may be unrealistic to expect that a scaffolding study have zero threats to validity due to the need to study scaffolding in contexts in which students can collaboratively solve authentic problems - contexts that do not include laboratories. In the three existing scaffolding meta-analyses, as methodological quality decreased, effect size magnitude increased (Lin et al., 2007; Swanson & Deshler, 2003; Swanson & Lussier, 2001). This highlights the need to code for methodological quality in a wider scaffolding meta-analysis.
Scaffolding and assessment level
When evaluating scaffolding's effectiveness, one needs to consider assessment level (e.g., concept, principles, and application) (Messick, 1989; Sugrue, 1995). For example, a fact-based test may not be the best vehicle to evaluate scaffolding designed to promote problem solving ability.
Scaffolding guidelines exist in such areas as online discussion (Choi, Land, & Turgeon, 2005), science inquiry (Linn, 2000; Quintana et al., 2004), problem solving (Ge & Land, 2004; Kolodner, Owensby, & Guzdial, 2004), and argumentation (Belland, Glazewski, & Richardson, 2008; Jonassen & Kim, 2010). Authors often gather empirical support for guidelines (Belland, 2010; Ge & Land, 2003), but the sheer volume of scaffolding frameworks and conflicting advice leads one to desire a comprehensive assessment of scaffolding. Furthermore, the effect of a specific feature inspired by a guideline is rarely isolated in empirical studies. The lack of clarity is not due to a lack of research, as there is a staggering volume of empirical research on scaffolding. Individual empirical studies are the engine that drives educational research, but in accumulating thousands of studies on scaffolding, researchers run the risk of "knowing less than we have proven" (Glass, 1976, p. 8). Meta-analysis is one way to avoid this risk (Chambers, 2004; Glass, 1976).
In this preliminary meta-analysis, we address the following research questions:
1. To what extent do scaffolding characteristics (strategy, intended outcome, fading schedule, intervention, and paired intervention) influence cognitive outcomes?
2. To what extent does methodological quality (study design, internal threats to validity, external threats to validity, and test score validity and reliability) influence cognitive outcomes?
3. To what extent does assessment level (concept, principle, application) influence cognitive outcomes?
4. To what extent do the combination of internal threats, external threats, reliability, fading, and scaffolding intervention influence cognitive outcomes?
To be included, studies needed to (a) cover primary, middle level, secondary, college/vocational, graduate/professional, and adult students, (b) compare a scaffolding treatment with a comparison condition (absence of scaffolding), (c) report quantitative, cognitive outcomes (e.g., problem-solving ability, conceptual understanding), and (d) report enough data to support effect size calculation. When more than one source reported the same data (e.g., a dissertation and a journal article), the source with the most detail (e.g., dissertation) was included.
The literature search began with a comprehensive review of scaffolding strategies (Kali & Linn, 2008) before proceeding to existing meta-analyses (Lin et al., 2007; Swanson & Deshler, 2003; Swanson & Lussier, 2001). Ninety-four studies were identified as candidates for inclusion on first pass. Upon application of the inclusion criteria by two research team members to each study identified as candidates for inclusion, the 94 studies were reduced to seven, for reasons including (a) not enough information to calculate an effect size, (b) lack of quantitative, cognitive outcomes, (c) lack of a control condition in which students did not receive scaffolding, and (d) the intervention did not meet the definition of computer-based scaffolding.
Two coders independently coded each study for several characteristics (see Table 1).
Given the diversity of research quality, interventions, populations, and sample sizes among existing primary research, effect size estimate precision varied. Thus, a conversion was made from Cohen's d to Hedges' g for all outcomes (Cooper, 1989).
To examine potential bias a funnel plot was generated. Figure 1 shows standard error and Hedge'sg for each study. The figure shows a general lack of symmetry suggesting publication bias. To investigate further, a cumulative forest plot (Sutton, 2009) was run. The nine most precise studies (g = 0.33) have a substantially lower estimate than all 17 studies (g = 0.53). This implies bias either due to publication or small study effects. This is somewhat anticipated, as the primary source of studies for this meta-analysis was existing reviews that tend to not cover "grey literature" such as book chapters, conference papers, or dissertations (Borenstein, Hedges, Higgins, & Rothstein, 2009). Only one included study was a dissertation; the rest were peer-reviewed journal articles.
Research questions 1-3 were addressed with a 0-test based on analysis of variance. Pairwise differences were assessed using a Z-test (Borenstein et al., 2009). For research question 4, meta-regression was employed. Since the studies in this analysis draw from fundamentally different populations a random effects model was employed. All significance testing assumed an alpha level of .05. Data analysis was conducted using STATA 11.
Before addressing our research questions, we present overall analyses. Seven studies with 17 outcomes met our inclusion criteria (Bagno & Eylon, 1997; Clement, 1993; Foley, 1999; Mayer, 1989; Nathan, Kintsch, & Young, 1992; Ronen & Eliahu, 2000; White & Frederiksen, 1998). Covered subject areas/grade levels are: high school physics (2 studies), middle school physical science (1 study), college mathematics (1 study), college mechanics (1 study), high school science (1 study), and middle and high school science (1 study). An l-squared test indicates a relatively high level of inconsistency (See Figure 2) across effect size estimates (78.0%, p = 0.001). The overall effect size (g = 0.53) is statistically significant, z = 4.65, p = 0.001, and of a medium magnitude (Cohen, 1988).
Boxed Hedges' g values are statistically greater than zero, p < 0.05. The forest plot shows estimates and confidence levels for each outcome. The diamond is a summary estimate and confidence interval for the overall effect.
Research question 1: To what extent do scaffolding characteristics (strategy, intended outcome, fading, intervention, and paired intervention) influence cognitive outcomes?
Table 2 shows point estimates and 95% confidence intervals for Hedge's g for each coded scaffolding characteristic. Examining strategy, one notices that there was no significant difference in cognitive outcomes between generic and context-specific scaffolds, and that the effect size for each is significantly greater than 0 p < 0 .05. This suggests that there are no differences in influence on cognitive outcomes between generic and context-specific scaffolds, though caution is warranted as only one coded study included a generic scaffold.
There were no significant differences between scaffolding that supports higher-order skills and scaffolding that supports knowledge integration (see Table 2). This provides preliminary evidence that scaffolding designed to support each intended outcome is equally effective. As the confidence intervals are fairly wide, examining more studies is necessary to have great confidence in no significant differences.
Turning to Fading Schedule in Table 2, studies in which scaffolding was not faded had higher effect sizes (g = 0.79) than studies that employed fixed fading (g = 0.20), z(16) = 3.02, p = .001. This implies that fixed fading harms cognitive outcomes (see Figure 3).
Note boxed Hedges'g values are statistically greater than zero, p < .05. Diamonds are summary scores, representing Hedges' g and confidence intervals for two or more outcomes; the final diamond represents all outcomes. "Lower" and "upper" represent the effect sizes' 95% confidence interval limits.
As indicated in Table 2 and Figure 4, studies using conceptual (g = 0.67) scaffolds exhibited superior learning outcomes to those using metacognitive (g = 0.25) scaffolds, z (16) = 3.29, p = .01. This emergent result should be interpreted with caution as only one study used metacognitive scaffolds.
Boxed Hedges' g values are statistically greater than zero, p < .05. Diamonds are summary scores, representing Hedges' g and confidence intervals for two or more outcomes; the final diamond represents all outcomes. "Lower" and "upper" represent limits of the effect sizes' 95% confidence intervals.
There were no significant differences between studies based on paired intervention: Inquiry-based learning led to an effect size of 0.39 and problem-solving led to an effect size of 0.53 (see Table 2). Of note, scaffolding is used in the context of other instructional approaches, so it is important to examine more studies covering a wider range of paired interventions.
Research question 2: To what extent does methodological quality (study design, internal threats to validity, external threats to validity, and test score validity and reliability) influence cognitive outcomes?
As indicated in Table 3, there were no statistically significant differences between study designs. Results from quasi- experimental designs parallel results of true random experiments, although the confidence intervals would likely shrink with the inclusion of more studies.
None of the studies reported validity data. Thus, no difference could be calculated based on validity reporting.
Turning to reliability reporting (See Table 3), one sees that studies reporting no reliability data had larger effects than studies with strong reliability reporting, z(16) = 2.11, p = .04. However, this result should be interpreted with caution as only one study reported reliability information.
Examining Internal Threats to Validity in Table 3, one notices that studies reporting no internal threats to validity had smaller learning gains than studies reporting two threats, z(16) = 2.68, p = .01. No other statistically significant differences were found.
Despite a range of effect size estimates, only studies with zero or one threat to external validity had values statistically greater than 0. In the only significant pairwise comparison (see Figure 5), studies with one external threat to validity had a higher effect size estimate than studies with no threats to validity, z(8) = 3.29, p = .001. In contrast to internal threats, there does not appear to be a systematic trend for external threats to validity.
Boxed Hedges' g values are statistically (p < .05) greater than zero, p < .05. Diamonds are summary scores, representing Hedges' g and confidence intervals for two or more outcomes; the final diamond represents all outcomes.
Research question 3: To what extent does assessment level (concept, principle, application) influence cognitive outcomes?
There were no significant differences between assessment levels (see Table 4). However, trends favored Application and Principles-level over Concept-level. With more studies included, the confidence intervals may shrink and significant differences would emerge.
Research question 4: To what extent do the combination of internal threats, external threats, reliability, fading, and scaffolding intervention influence cognitive outcomes?
Regression was used to determine how these variables combine to influence cognitive learning. This involved stepwise regression (forward) selection because including all data with backward elimination would likely result in overfitting due to the small number of observations. A Bonferroni correction was applied for more stringent selection criteria (t = 1.18) given the number of variables (Foster & George, 1994). Only variables with statistically significant differences were considered as predictor candidates--reliability reporting, threats to internal validity, threats to external validity, fading, and scaffolding intervention. The latter two were both dummy coded. The final model explains a statistically significant portion of the variance, [R.sup.2] = .30, p = .02 and consists of only a single variable with two levels: fixed fading, or no fading. When scaffolds were not faded, students had higher cognitive outcomes (t = 2.63). Scaffolding intervention explained 30% of the variability in cognitive outcomes.
Overall, scaffolding produced a medium effect (g = 0.53). There were no differences based on study design, assessment level, or intended learning outcome. When examined individually, effect size differences were predicted by reliability reporting, threats to internal validity, external threats to validity, fading, and scaffolding intervention (conceptual or metacognitive). No study reported validity information, and only one study reported reliability information. Studies with zero threats to internal validity had lower effect sizes than studies with two threats. Studies with one threat to external validity had higher effect sizes than studies with zero threats to validity. Studies with no fading had higher effect sizes than studies with fixed fading. Students fared better with conceptual scaffolds than with metacognitive scaffolds. When significant predictors were entered into a regression analysis, fading explained 30% of the variability in cognitive outcomes: students did better in studies with no fading than in studies where scaffolding was faded according to a fixed schedule.
One of the most interesting findings was that students fared better when scaffolding did not fade than when it faded on a fixed schedule. The limited number of studies using fixed fading should be noted; including more studies in a larger meta-analysis would help to see if the pattern is consistent. For years, researchers have argued that computerbased scaffolds must fade (Pea, 2004; Puntambekar & Hubscher, 2005). Fading was originally proposed to enable transfer of responsibility from the scaffolding provider to the student (Collins, Brown, & Newman, 1989). According to this reasoning, after fading, students would be able to perform the target task independently. All would agree that promoting transfer of responsibility is important. Some have questioned the theoretical and empirical evidence that fixed fading really leads to transfer of responsibility (Belland, 2011). Without ongoing diagnosis, fading is implemented according to a "best guess" of when scaffolds should be removed by either a designer before a student even uses the scaffold, or by a student as she/he uses the scaffold. Thus, fixed fading may cause scaffolds to be removed (a) too soon, resulting in insufficient support, or (b) too late, resulting in a requirement to use scaffolds when students understand how to perform the target task. In short, if students learn less when using scaffolding with fixed fading, and one cannot be sure that transfer of responsibility happens with fixed fading, then fixed fading becomes less attractive as a scaffolding feature. However, it should be noted that another strategy of fading computer-based scaffolds was not covered in this study--making scaffolds disappear when students indicate they do not need them any more (e.g., Metcalf, 1999).
Results for internal threats to validity and external threats were also interesting. Studies with 2 threats to internal validity had higher effect sizes than studies with 0 threats to internal validity. To promote maximum ecological validity of scaffolding studies among secondary students, studies should be done in middle and high schools. K-12 school administrators and Institutional Review Boards rarely allow individual K-12 students to be randomly assigned to treatments. Thus, research that is conducted in secondary schools will encounter at least one threat to internal validity. Furthermore, by studying scaffolding in laboratory settings where students can be randomly assigned as individuals to treatment conditions, one would lose the essence of scaffolding, as scaffolding is meant to be deployed in authentic, collaborative problem solving or other contexts that require more than just filling out a worksheet or passively listening to or watching instructional materials (Belland, 2014; Wood et al., 1976).
Students did better when there was one external threat to validity than when there were no threats. The two most common threats to external validity in this study were limited description of the scaffolding intervention and experimenter effect. By limited description, we mean that there was enough to determine that it was scaffolding but not enough to replicate the study. A sufficient study procedure description would not change the effect size. Experimenter effect was noted if there was only one instructor. This is common, because early studies of educational interventions often involve a partnership between researchers and one or two teachers (Anderson & Shattuck, 2012). Because teachers do not often design interventions developed in design-based research (Anderson & Shattuck, 2012), fear of artificial inflation of effect sizes through teacher actions is relatively unfounded.
There was a significant difference in cognitive outcomes between conceptual and metacognitive scaffolds. There are two possible explanations: (1) students often do not use metacognitive computer-based scaffolds (Oliver & Hannafin, 2000), and (2) we examined how scaffolding influences cognitive outcomes. Metacognitive scaffolds may lead to other important outcomes like self-regulated learning ability.
No significant differences
Often it is forgotten that a lack of significant differences can be, in itself, substantial. We did not find any significant differences based on intended learning outcome, assessment level, generic versus context-specific, paired intervention, or study design.
The lack of significant differences based on intended learning outcome provides preliminary evidence that scaffolding works equally well in support of higher-order learning and knowledge integration outcomes. This is encouraging in that students need to gain problem solving ability to be successful in the 21st century workplace (Feller, 2003; Nordgren, 2002), but they also need to recognize that concepts and theories learned in school apply outside of school (Linn, 2000).
Our finding of no significant difference according to assessment level is interesting when compared alongside our previous research in which we found that outcomes of PBL varied based on assessment level (Belland, Walker, Leary, Kuo, & Can, 2010). The inclusion of more studies would help to further elucidate this difference.
That there was no difference in cognitive outcomes between generic scaffolds and context-specific scaffolds is interesting in that it would help scaffold designers make an informed choice about whether to base scaffolding on a generic process or specific content.
It is interesting that there was no difference in learning outcomes according to paired intervention. More studies should be included in a future meta-analysis such that more paired interventions can be included and it can be seen if the trend of no difference in cognitive outcomes holds.
Finally, it is encouraging that there was no difference in cognitive outcomes based on study design because quasi- experimental is the most common quantitative research design in educational research (Gall et al., 2003). This suggests that quasi-experimental designs sufficiently capture the magnitude of scaffolding's effect.
While there was a significant difference in study outcomes based on test score reliability, the fact that only one study reported reliability of test scores calls into question the interpretability of this effect. The validity and reliability reporting rate in this study roughly parallels the proportion of PBL studies that reported validity and/or reliability of test scores (Belland, French, & Ertmer, 2009). Unfortunately, reliability and validity reporting is sparse throughout educational research (Belland et al., 2009; Hamdy et al., 2006; Hogan & Agnello, 2004). Readers should recall that reliability and validity reporting should be done not just because the American Educational Research Association (2006) mandates it, but because it has serious consequences for study interpretation. For example, low reliability can lead researchers to under-estimate an effect's magnitude (Hunter & Schmidt, 2004; Loevinger, 1954). If one knows the reliability coefficient, one can estimate the true effect. Low reliability also negatively impacts validity, because one cannot reliably predict what a person with a particular competence level on Trait X will get on an unreliable test that measures Trait X (Messick, 1989). Invalid test scores cannot indicate how much students have of the target construct (Anastasi & Urbina, 1997). Most importantly, improper validity and reliability reporting can lead to improper theory construction based on erroneous empirical results. Improper theory construction has serious consequences, because it can cause school districts and other governmental agencies to spend scarce resources on suboptimal learning tools.
Limitations and suggestions for future research
The final number of included studies is a limitation. We started with 94 studies, but 7 studies were included in the meta-analysis. Eliminated studies (a) did not contain sufficient information to code for an effect size and associated information, (b) did not have an appropriate control group, or (c) were deemed to not describe a scaffolding intervention. Such elimination required agreement of at least two researchers. Including more studies may have led to different results. Loosening inclusion criteria is not a good choice to increase the number of included articles. Rather, broadening search strategies is. This study was a preliminary meta-analysis intended to optimize our coding and analysis procedures and get a sense of important trends in the scaffolding literature. As such, we limited our selection of studies to those harvested from existing literature reviews. As a next step in our research program, we will conduct a comprehensive search of the primary literature (Cooper, Hedges, & Valentine, 2009). Future research may include search terms describing tools similar to scaffolds, such as mindtools and intelligent tutors. Also, future research should take care to broaden search databases to include ones with greater coverage of the non-USA literature.
Meta-analyses only can include certain quantitative studies. Qualitative studies are common in the scaffolding literature. Empirical studies of a variety of designs and methodologies are all of great value, and all can contribute to an understanding of the impacts of scaffolding. Effect sizes calculated during meta-analyses cannot reflect all pertinent literature on scaffolding. Nonetheless, they can help direct future research and development.
Conclusion and implications
The significance of this paper lies in its affirmation of scaffolding as an effective intervention, and its guidance of future research on scaffolding, as well as of data-driven scaffolding design decisions. First, we found preliminary evidence that scaffolding is effective, producing an average effect size of 0.53. Scaffolding's effect (0.53) is (a) considerably stronger than that of the average instructional intervention designed to promote critical thinking (0.341) (Abrami et al., 2008), yet (b) lower than that found for one-to-one human tutoring (0.79) in a recent meta-analysis (VanLehn, 2011). Still, given the high student-to-teacher ratios in most K-12 schools, and the focus on improving critical thinking abilities of the Common Core State Standards and the Next Generation Science Standards, scaffolding may be a particularly promising intervention (Achieve, 2013; National Governors Association Center for Best Practices & Council of Chief State School Officers, 2010).
Scaffolding produced higher effect sizes when studied in authentic settings (e.g., classroom-based problem-based learning) in which there are more threats to internal and external validity. Thus, educators can have confidence in scaffolding's efficacy even when studies suffered from threats to internal or external validity.
Conceptual scaffolding produced higher effect sizes than metacognitive scaffolds. Scaffolding with no fading produced larger effects than scaffolding with fixed fading. This reinforces the role of teachers in supporting metacognition and transfer of responsibility.
We found preliminary evidence that scaffolding's effectiveness does not depend on whether it (a) supports higher-order outcomes and knowledge integration, (b) is generic or context-specific, (c) is used with different paired interventions, or (d) is assessed at the concept, principles, or application level. These findings may imply that on these characteristics, teachers can select scaffolding that best aligns with learning goals.
(Submitted October 8, 2013; Revised March 15, 2014; Accepted April 10, 2014)
This work was supported by a grant from Utah State University. Any opinions, findings, and conclusions are those of the authors.
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Brian R. Belland (1) *, Andrew E. Walker (1), Megan Whitney Olsen (1) and Heather Leary (2)
(1) Department of Instructional Technology and Learning Sciences, Utah State University, USA // (2) Institute of Cognitive Science, University of Colorado-Boulder, USA // firstname.lastname@example.org // email@example.com // firstname.lastname@example.org // email@example.com
* Corresponding author
Table 1. Coding Scheme Contextual Information Paired intervention (e.g., problem-based learning) (a) Assessment level--conceptual, principles, application (Sugrue, 1995) Education level (e.g., middle level) Discipline (e.g., physical science) Collection year Institution name (of the primary author) Attrition treatment and attrition control (% of total assigned to treatment or control conditions) Study design (e.g., random, group random, or quasi experimental) Scaffold intervention Scaffolding strategy (e.g., make thinking visible) (Kali & Linn, 2008) Scaffolding function (e.g., reduce complexity) (Wood et al., 1976) Scaffolding intervention (e.g., conceptual) (Hannafin et al., 1999) Fading (none; human-adapted; fixed; self-selected) Generic or specific (a) Scaffolding Outcome (higher-order thinking skills or knowledge integration) (a) Required information for effect size calculation Means, SD, or ANOVA or other statistical comparison test values N for treatment and control Threats to internal validity (Gall et al., 2003) (b) History Maturation Testing Instrumentation Statistical regression Differential selection Experimental mortality Threats to external validity (Shadish & Myers, 2001) Limited description of treatment Multiple treatment interaction Experimenter effect Test Score Quality Reporting (c) Test score validity reporting Test score reliability reporting Note. (a) Bolded coding items indicate a category that was added or heavily modified as a result of pilot coding. (b) Internal validity and external validity threats were coded for the degree to which the threat could account for study results; 0 meant not a plausible threat and 3 meant it could account for all variance in the outcome. (c) Test score quality reporting was assessed as (a) strong, if authors reported validity or reliability data for their own sample, (b) attempt, if authors reported validity or reliability data from other studies, or (c) none, if no validity or reliability data was reported. Table 2. Influence of scaffolding characteristics on cognitive outcomes Scaffolding [N.sub.studies] [N.sub.outcomes] g Characteristic Strategy Generic (a) 1 1 0.72 Specific (a) 6 16 0.52 Intended Outcome Higher order skills (a) 2 7 0.44 Knowledge integration (a) 5 10 0.59 Fading Schedule None (a) 6 10 0.79 Fixed 2 7 0.20 Scaffolding Intervention Conceptual (a) 6 12 0.67 Metacognitive 1 5 0.25 Paired Intervention Inquiry-based learning (a) 3 12 0.39 Problem solving (a) 4 5 0.53 Scaffolding [CI.sub.Lower] [CI.sub.Upper] Characteristic Strategy Generic (a) 0.41 1.03 Specific (a) 0.28 0.75 Intended Outcome Higher order skills (a) 0.31 0.74 Knowledge integration (a) 0.27 0.90 Fading Schedule None (a) 0.47 1.11 Fixed 0.00 0.41 Scaffolding Intervention Conceptual (a) 0.37 0.97 Metacognitive 0.00 0.50 Paired Intervention Inquiry-based learning (a) 0.16 0.61 Problem solving (a) 0.31 0.75 Note. (a) Significantly greater than an effect size of 0. p < .05. Table 3. Influence of methodological quality on cognitive outcomes Methodological Quality [N.sub.studies] [N.sub.outcomes] Indicator Study Design Quasi-experimental (a) 3 3 Group random (a) 2 8 Random (a) 2 6 Validity Reporting None (a) 7 17 Attempt 0 0 Strong 0 0 Reliability Reporting None (a) 6 11 Attempt 0 0 Strong (a) 1 6 Number of Internal Threats (b) None 2 5 One 0 0 Two (a) 3 4 Three (a) 4 8 Number of External Threats (c) None (a) 1 5 One (a) 2 4 Two 2 3 Three 2 2 Four 1 3 Methodological Quality g CJ Lower CJ Upper Indicator Study Design Quasi-experimental (a) 0.60 0.16 1.05 Group random (a) 0.43 0.13 0.73 Random (a) 0.66 0.19 1.13 Validity Reporting None (a) 0.53 0.30 0.75 Attempt -- -- -- Strong -- -- -- Reliability Reporting None (a) 0.67 0.37 0.97 Attempt -- -- -- Strong (a) 0.25 0.01 0.50 Number of Internal Threats (b) None 0.29 -0.11 0.68 One -- -- -- Two (a) 0.89 0.67 1.09 Three (a) 0.45 0.16 0.74 Number of External Threats (c) None (a) 0.25 0.00 0.50 One (a) 1.00 0.63 1.37 Two 0.36 -0.35 1.07 Three 0.44 -0.10 0.98 Four 0.83 -0.13 1.79 Note. (a) Significantly greater than an effect size of 0, P < .05. (b) [N.sub.studies] does not add up to 7 because internal threats are associated with individual outcomes. Some studies included more than one outcome. (c) [N.sub.studies] does not add up to 7 because external threats are associated with individual outcomes. Some studies included more than one outcome. Table 4. Influence of Assessment Level on Cognitive Outcomes. Assessment [N.sub.studies] [N.sub.outcomes] Level (b) Concept 3 6 Principles (a) 6 8 Application (a) 2 3 Assessment g CJ Lower CJ Upper Level (b) Concept 0.46 0.00 0.92 Principles (a) 0.54 0.20 0.88 Application (a) 0.63 0.34 0.93 Note. (a) Significantly greater than an effect size of 0, P < .05. (b) [N.sub.studies] does not add up to 7 because some studies employed more than one test that covered two or more assessment levels. Figure 2. Outcomes Study/Eftect n treat n cntrl Hedges'g (Bagno & Eylon, 1997) 69 111 0 72 transfer--concepts (Clement, 1993) static, 150 55 0.93 friction, 3rd law (Foley, 1999) post-test 79 77 0.17 (Ronen & Eliahu, 2000) 71 74 0.00 theoretical exam (White & Frederiksen, 118 119 0.00 1998) explanation of applied physics test multiple choice applied 118 119 0.00 physics test conceptual model test 30 30 0.45 Mass project 60 60 0 47 inquiry test 45 45 0.55 (Mayer, 1989) Exp. 1 17 17 0.00 verbatim recognition Exp. 1 non-explanative recall 17 17 0.00 Exp. 1 explanative recall 17 17 0.65 Exp. 1 transfer 17 17 102 (Nathan etal., 1992) post-test 14 14 1.04 training 14 14 144 (Ronen & Eliahu, 2000) task 34 29 101 2--on last trial task 1 13 29 1-61 Overall 883 844 0.53 Figure 3. Learning by fading Fading studies outcomes Hedges' g lower upper none 6 10 0.79 0.47 1.11 fixed 2 7 0.20 0.00 0.41 Overall 7 17 0.53 0.31 0.75 Figure 4. Learning by scaffolding intervention Intervention studies outcomes Hedges' g lower upper conceptual 5 12 0.67 0.37 097 metacognitive 1 5 0.25 0.00 0.50 Overall 7 17 0.53 0.31 0.75 Figure 5. Learning by external threats External Threats studies outcomes Hedges g lower upper none 1 5 0.25 0.00 0.50 one 2 4 1.00 0.63 1.37 two 2 3 0.36 -0.35 1.07 three 2 2 0.44 -0.10 0.98 four 1 3 0.83 -0.13 1.79 Overall 7 17 0.53 0.1 0.75
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|Author:||Belland, Brian R.; Walker, Andrew E.; Olsen, Megan Whitney; Leary, Heather|
|Publication:||Educational Technology & Society|
|Date:||Jan 1, 2015|
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