Evidence map›Paper›PMID 35602677›Full record

ArticleFrontiers in psychology2022

Raising the Stakes for Online Learning: Monetary Incentives Increase Performance in a Computer-Based Learning Task Under Certain Conditions.

Jessica F Schwab, Leah H Somerville

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jessica F SchwabDepartment of Psychology, Harvard University, Cambridge, MA, United States.
Leah H SomervilleDepartment of Psychology, Harvard University, Cambridge, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To what extent can external incentives influence students' effort and learning in online course contexts? While cognitive science research has found that monetary incentives can increase goal-directed cognitive effort in certain laboratory tasks, attempts to use monetary incentives to increase students' academic performance in naturalistic settings has shown mixed results. In two experiments, we tested the influence of a monetary incentive (compared to no external incentive) on immediate and delayed tests of computer-based educational performance (i.e., learning from educational videos). In Experiment 1, participants were assigned to (1) receive monetary incentives for correct quiz responses, or (2) receive no additional incentive for correct responses other than finding out their score, and we found no significant difference in total score across groups (on either immediate or delayed tests of learning). In Experiment 2, we used a within-subjects design to test whether participants performed better when they were provided monetary incentives for correct responses on quiz questions (compared to no external incentive). Here, participants performed significantly better on incentivized quiz questions (on both immediate and delayed tests of learning). Thus, monetary incentives may increase performance in online learning tasks when participants can anchor the "stakes" of an incentive compared to no external incentive. These findings highlight potential benefits of external incentives for promoting effort and learning in online contexts, although further research is needed to determine the most useful educationally-relevant extrinsic incentives, as well as potential negative effects of incentives on long-term intrinsic motivation.

Indexed as

cognitiongoal-directedincentivesonline learningstakes

Identifiers

PMID35602677
PMCPMC9119014

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.