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

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

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

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

5. 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

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

7. 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

8. 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

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

10. 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

11. 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

12. 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

13. Results: Funnel Plots
Learning Outcomes
Learning Gains

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

15. 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

16. 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

17. Thank You!