Break large homework assignments into smaller, frequent, short-duration daily tasks¶

Strategy · All strategies
Evidence · 4 claims (3 for, 1 mixed) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 4 claims rest on one study

Description¶

The authors recommend that educators restructure homework so that it is assigned often but kept short, rather than given as long problem sets. They "recommend that educators should consider breaking down large homework assignments into smaller, daily tasks that offer regular and spaced opportunities to engage with the material". This provides a middle ground in which students gain the achievement benefits of frequent homework while retaining evening time for other pursuits and avoiding stress and burnout.

Design Implications¶

Context¶

Requirements¶

• Teachers control both how often homework is assigned and how long each assignment takes; the frequency benefit was observed relative to a baseline of up to one or two times per week

Constraints¶

• Evidence comes from eighth-grade mathematics and science students in Ireland; effects differ by subject, so daily frequency is supported for mathematics while three or four times per week was optimal for science

Target Learners¶

• eighth-grade students

Target Learning Goals¶

• mathematics achievement
• science achievement

Claims¶

• Daily mathematics homework raises eighth-grade mathematics achievement by about 7.36 TIMSS units relative to homework up to one or two times per week [+W]
• Increasing homework duration beyond fifteen minutes shows no clear benefit for mathematics or science achievement [+W]
• Homework frequency is more important than homework duration for improving eighth-grade mathematics and science achievement [+W]
• The optimal homework frequency in science is three or four times per week, with daily science homework showing no significant benefit over the baseline [~W]

Related Strategies¶

• Break Tasks Into Pieces
• Adjust Time Frames
• Time Boxing
• Achievable Micro-goals

Examples¶

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Key Sources¶

• Nathan McJames, Andrew Parnell, and Ann O'Shea. (2023). Little and Often: Causal Inference Machine Learning Demonstrates the Benefits of Homework for Improving Achievement in Mathematics and Science. https://timssandpirls.bc.edu/timss2019/international-results