Domain-Specific Prior Knowledge and Learning: A Meta-Analysis Bianca A. Simonsmeier Maja Flaig Anne Deiglmayr Lennart Schalk Michael Schneider

Prior Knowledge “the most important single factor influencing learning is what the learner knows already” Ausubel, 1968

Effects of Prior Knowledge on Learning Positive Guides attention Helps interpreting and understanding Aids encoding in memory … Negative Misconceptions Einstellung effect Interference Negative transfer

Prior Achievement & Learning Outcomes Relationship Correlations School readiness – later school achievement (Duncan et al., 2007; La Paro & Pianta, 2000) .10 - .48 Prior school grade – later school grade (Trapmann et al., 2007) .19 - .31 GPA/GRE – GPA (Kuncel, Hezlett, & Ones, 2001; Schuler, Funke, & Baron-Boldt, 1990) .25 - .46 GPA – adult/occupational achievement (Schuler et al., 1990; Samson et al. 1990; Bretz 1989; Cohen 1984) .16 - .41

Constructs Domain-specific prior knowledge (T1) Dependent variables CK Information in long-term memory at the onset of a learning phase Relating to the key principles in a domain (e.g., mathematical equivalence) Dependent variables Knowledge & achievement Individual differences in learning outcomes at T2 Individual differences in learning gains from T2 to T1

Moderators Time 1 Learning phase Time 2 Environment Learner Similarity Prior Knowledge Learning Outcome Time 1 Learning phase Time 2

Research Questions Effects of prior knowledge on later learning outcomes (T2)? Effects of prior knowledge on learning gains (T2-T1)? Moderators? Knowledge characteristics Learner characteristics Environmental characteristics Methodological study characteristics

Literature Search All age groups All content domains Two time points Knowledge at T1 K or achievement at T2 Screened ≈ 5000 titles/abstracts Screened ≈ 500 full texts Included 240 studies

Included Data Data 4327 effect sizes 240 articles 62,129 participants

Method Inter-coder agreement 90% All effects sizes transformed into correlations Corrected for measurement error and dichotomization Random-effects meta-analysis Robust Variance Estimation (RVE; e.g., Tanner-Smith & Tipton, 2014) Robumeta package in R

Results: Learning Outcomes Effect Studies Effect sizes r+ 95% CI Overall learning gains 14 33 -.08 [-.48, .34] Overall learning outcomes 235 4223 .53 [.50, .55] Randomized controlled experiments No 226 4279 [.50, .56] Yes 9 44 .31 [.19, .42] Controlling for intelligence Before controlling 28 1305 .52 [0.46, 0.57] After controlling .48 [0.42, 0.54] Normalized learning gains reported

Results: Significant Moderators Knowledge characteristics Similarity of prior knowledge and learning outcome .021 Knowledge type .037 Learner characteristics Educational level .059 Environmental characteristics Cognitive demands of intervention .056 Instructional method: Problem based learning .012 Methodological characteristics Number of items in prior knowledge measure .036 Number of items learning outcome measure .025

Results: Funnel Plots Learning Outcomes Learning Gains

Limitations Quantity vs. quality of knowledge? Some moderator levels underrepresented (e.g. chemistry, medicine, procedural knowledge) Limited research on knowledge gains Few experiments

Open questions/ current revision Literature Search Keywords to identify gain studies Include unpublished studies vs. possibility to screen results (+3000 dissertations only) Meta-analytic integration Combine different effect sizes (e.g. from independent and dependent designs) Combine data from growth models

Conclusion and implications Prior knowledge has high predictive validity for learning outcomes Conceptual and statistical differences between learning outcomes and learning gains Limited amount of studies examining learning gains Effect of prior knowledge depend on a variety of moderators Use formative assessment, activate prior knowledge, and adapt instruction to students prior knowledge

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