Motivation: To conduct a meta-analysis on the within-subjects effects (i.e., learning gains) of computer-based scaffolding.

Scaffolding: Scaffolding in construction is a temporary structure that allows workers to build the upper portion of a building before it can support itself. Scaffolding in learning is very similar. It is temporary support, provided by instructors or instructional materials, that allows learners to build advanced knowledge and skills until the extra support is no longer needed. Belland et al. discuss three key attributes of scaffolding:

• Contingency – the support offered through instruction is contingent on the current level of skill and needs of the learner.
• Intersubjectivity – scaffolding necessarily involves a relationship between the learner and the source of scaffolding.
• Transfer of responsibility – scaffolding provides support to learners in the earlier stages of learning, but it fades until ultimately the learner completes the task independently.

Though research on scaffolding has been popular since the 70s (e.g., Wood, Bruner, & Ross, 1976), much modern research focuses on leveraging computers to provide scaffolding. “Computer-based scaffolding is often designed to a) help students with what to consider when addressing a problem (conceptual scaffolding), b) bootstrap a strategy for addressing a problem (strategic scaffolding), c) invite students to question their own understanding (metacognitive scaffolding), and d) enhance interest, autonomy, self-efficacy, and other motivational variables (motivation scaffolding; Belland, Kim, & Hannafin, 2013; Hannafin, Land, & Oliver, 1999; Rienties et al., 2012)” (p. 1044).

Purpose and method: In contrast to other meta-analyses on scaffolding that compare scaffolded instruction to unscaffolded instruction, this meta-analysis examined the learning gain of students from before scaffolding to after scaffolding (i.e., pre-post learning gains). The purpose of the research was to determine in which contexts scaffolding is more or less effective and the average benefit of scaffolded instruction. The analysis considered several contextual features:

• Age level of learner – primary, middle, secondary, college, and graduate
• Education population – traditional, high performing, low performing, English language learners, underrepresented, and learning disabilities
• Problem-centered instructional model – problem solving, project-based learning, inquiry-based learning, problem-based learning, design-based learning, and modeling/visualization
• STEM discipline – science, technology, engineering, and math
• Assessment type – concept (basic knowledge), principle (relational knowledge), and application (apply knowledge to problem solving)

Belland et al. used a Bayesian-based network meta-analysis approach on the pre-post scores of 56 studies.

Results: Across different age levels, scaffolding had a consistently strong effect (avg g = ~0.5 or greater), except for secondary students (avg g = 0.2). The largest effects  were for college students (avg g = 1.16) and graduate students (avg g = 1.20), but graduate studies had much more potential error than other age groups.

Across education populations, scaffolding had a consistently substantial effect (avg g = 0.4 or greater). Students with learning disabilities had the largest gains, with an astounding avg g of 3.13.

Across problem-centered instructional models, scaffolding had a consistently strong effect (avg g = 0.6 or greater), except for inquiry-based learning (avg g = 0.0) and modeling/visualization (avg g = 0.3). The highest ranking instructional model was project-based learning (avg g = 1.21), but it also had considerable potential error. Problem solving had a more modest (but still strong) avg g of 0.86 with little potential error.

Across STEM disciplines, scaffolding had a consistently strong effect (avg g = ~0.5 or greater). The highest ranking subjects were math (avg g = 1.29) and technology (avg g = 1.06), but technology had high potential error.

Across assessment type, scaffolding had an equally strong effect for concept, principle, and application level assessments (avg g = 0.7-0.9).

Why this is important: This meta-analysis builds upon previous meta-analyses that suggest that scaffolded, problem-based instruction is more effective in STEM than other types of instruction (e.g., Belland et al., 2017). It tells us that computer-based, scaffolded, problem-based instruction is consistently effective across most age levels, educational populations, instructional models, STEM disciplines, and assessment types. Therefore, despite detractors who still advocate for pure direct instruction, an instructor can be relatively certain that a well-designed (as described in the literature review), computer-based scaffolding system for problem-based instruction is going to contribute to strong learning gains for students in STEM classes.

Belland, B. R., Kim, C., & Hannafin, M. J. (2013). A framework for designing scaffolds that improve motivation and cognition. Educational Psychologist, 48(4), 243-270.

Belland, B. R., Walker, A. E., & Kim, N. J. (2017). A Bayesian network meta-analysis to synthesize the influence of contexts of scaffolding use on cognitive outcomes in STEM education. Review of Educational Research, 87(6), 1042-1081.

Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017). Synthesizing results from empirical research on computer-based scaffolding in STEM education: A meta-analysis. Review of Educational Research, 87(2), 309-344.

Hannafin, M., Land, S., & Oliver, K. (1999). Open learning environments: Foundations, methods, and models. Instructional-design Theories and Models: A New Paradigm of Instructional Theory, 2, 115-140.

Rienties, B., Giesbers, B., Tempelaar, D., Lygo-Baker, S., Segers, M., & Gijselaers, W. (2012). The role of scaffolding and motivation in CSCL. Computers & Education, 59(3), 893-906.

Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100.

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