Self-Monitoring of Performance (SMP)¶

Strategy · All strategies
Evidence · 3 claims (1 for, 2 mixed) · 8 studies (4 quant-synthesis, 2 review, 1 causal, 1 theoretical), q1–q4 · 4 of 8 report an effect size

Description¶

Self-monitoring of performance is a strategy in which learners observe, record, and evaluate an aspect of their own academic performance — for example, the number of math problems completed correctly per minute, pages read, or accuracy on written work. The learner typically uses a simple recording tool (tally sheet, graph, checklist) against a clear performance standard. Unlike self-monitoring of attention or behavior, SMP targets the academic product itself, making it a core component of Self-Regulated Learning Self-monitoring is one of the most consistently effective self-regulation subprocesses for academic outcomes. [+M].

Design Implications¶

SMP works because it converts an invisible performance stream into visible, immediate feedback, which both informs the learner and functions as a reactive intervention — the act of recording alone often improves the monitored behavior Self-monitoring produces reactive effects on the monitored behavior. [+M]. It is most effective when the monitored dimension is one the learner can actually control (rate, accuracy, persistence) and when records are compared against an explicit criterion rather than left uninterpreted.

Context¶

Requirements¶

• A clearly defined, observable academic behavior or product to monitor (e.g., problems correct per minute)
• A low-effort recording method matched to the learner's age and skill (tally marks, graph, digital counter)
• An explicit performance standard or goal so the record can be evaluated, not just collected
• Learner training in how to count and record accurately before independent use
• A visible, specific performance standard: rubric, worked model, target metric, or answer key (Demonstration provides the model to monitor against)
• A lightweight recording mechanism — checklist, log, or self-scoring form — that does not itself consume working memory Cognitive overload degrades learning. [~M]
• Opportunities to act on what is monitored; monitoring must be followed by strategy adjustment or practice, not just recording
• Periodic external calibration (instructor feedback, peer check) to correct drifting self-judgments

Constraints¶

• SMP improves the application of existing skills rather than teaching new ones [-M] — a learner who cannot do the work will not benefit from tracking it
• Effectiveness drops when the monitored dimension is not under the learner's control or the standard is vague [-M]
• Recording adds working memory and time demands during tasks; for novices the monitoring burden can compete with the learning task itself [~W]
• Accuracy of self-recording degrades without periodic checks; inflated self-records are common when no verification exists [-W]
• Monitoring accuracy is poor for novices: they lack the knowledge to detect their own errors and often judge fluency as mastery [-M]
• Monitoring effort or time-on-task rather than quality of performance can reinforce unproductive persistence [~M]
• Over-monitoring (excessive check-ins, logging overhead) shifts attention from the task to the self and can undermine motivation Autonomy supports intrinsic motivation. [~M]

Implementation Variability¶

• Self-monitoring of accuracy vs. rate — accuracy suits quality goals; rate suits fluency goals
• Teacher- vs. learner-set goals — learner-set goals add motivation benefits but require goal-setting training
• Graphing — having learners plot their own data strengthens the feedback loop and sustains engagement
• Fading — recording prompts can be systematically withdrawn once target performance is stable (McDougall & Brady, 1998)
• Process monitoring (Did I follow the steps? Am I confused?) vs. outcome monitoring (Is the answer correct? Does the draft meet the rubric?) — process monitoring suits skill acquisition, outcome monitoring suits revision and study regulation
• Frequency: continuous (error detection during performance), interval (end of each work session), or milestone-based (end of each task stage)
• Recording mode: tally sheets and timers (behavioral), self-scoring against keys (cognitive), structured reflection prompts (metacognitive)
• Social forms: peer monitoring pairs and check-ins add external accountability and calibration

Target Learners¶

• Learners with underdeveloped self-regulation, including students with ADHD or learning disabilities Self-management interventions reliably improve academic performance for students with disabilities. [+M]
• Elementary through adult learners; procedures must be adapted to developmental level
• Less appropriate for learners who lack the target skill entirely — teach the skill first, then use SMP for fluency and consistency
• Adolescents and adults with enough metacognitive development to judge quality against a standard [+M]
• Novices benefit only when external standards (models, keys, rubrics) substitute for their missing internal expertise [-M]
• Advanced learners benefit most, since they can detect subtle discrepancies between their performance and expert standards Self-monitoring accuracy increases with domain expertise. [+M]

Target Learning Goals¶

• Fluency building: increasing rate of accurate responding on practiced skills
• Work completion and academic engaged time
• Metacognitive habit formation: establishing the monitor-and-evaluate cycle of Self-Regulated Learning
• Skill refinement: identifying and correcting specific performance errors
• Study regulation: allocating time and effort to the material most in need of work
• Metacognitive development: building durable habits of judging one's own understanding
• Transfer of self-regulation to independent, unsupervised performance

Instructions¶

1. Define the target performance and a measurable indicator (rate, accuracy, or completion).
2. Set an explicit performance criterion with the learner, ideally collaboratively.
3. Teach the recording procedure and have the learner Practice it until records are accurate.
4. Have the learner record performance during work sessions and compare results to the criterion.
5. Assess Performance periodically — verify self-records against teacher counts and adjust the criterion upward as performance improves.
6. Fade the recording supports once target performance is maintained independently.

Related Strategies¶

• Goal Setting — SMP supplies the progress data that makes goals actionable
• Self-Evaluation — the judgment step that follows the monitoring step in the self-regulation cycle
• Check-Ins — structured teacher–learner conversations that can review self-monitored data
• Self-reinforcement — the control-side counterpart: learners reward or adjust based on what monitoring reveals
• Use self-scoring against correct answers as a tool to foster primary students' monitoring accuracy during math problem-solving practice

Examples¶

• A student tallies the number of math facts answered correctly during a two-minute timing each day and graphs the results against a personal best (McDougall & Brady, 1998).
• A middle schooler records pages read during silent study periods and compares weekly totals to a negotiated goal.
• ClassDojo and similar behavior-tracking apps digitize the recording step, though SMP requires the learner, not the teacher, to do the recording for the self-regulation benefit to accrue.
• Self-scoring math practice: students check each problem against an answer key immediately, log error types, and choose the next problem set based on which error type dominates — immediate self-scoring improves subsequent study decisions [+M]
• Writing revision with rubrics: students score their own draft against a six-trait rubric before peer review, then revise the two lowest-scoring traits
• Reading fluency graphs: students chart words-correct-per-minute across repeated readings, making progress visible and motivating further practice [+M]
• Khan Academy — mastery progress bars and skill dashboards externalize performance monitoring, showing learners exactly which skills remain below mastery

Key Sources¶

• McDougall, D., & Brady, M. P. (1998). Initiating and fading self-management interventions to increase math fluency. Journal of Applied Behavior Analysis, 31(1), 35–46. doi:10.1177/001440299806400201
• Reid, R., Trout, A. L., & Schwartz, M. (2005). Self-regulation interventions for children with attention deficit/hyperactivity disorder. Focus on Exceptional Children, 32(4), 361.
• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. doi:10.1207/s15430421tip4102_2
• Mooney, P., Ryan, J. B., & Uhing, B. M. (2005). A review of self-management interventions targeting academic outcomes for students with emotional and behavioral disorders. Journal of Behavioral Education, 14(3), 203–221. doi:10.1007/s10864-005-6298-1
• Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281. doi:10.3102/00346543065003245
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58. doi:10.1177/1529100612453266
• Sitzmann, T., & Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training. Psychological Bulletin, 137(3), 421-442. doi:10.1037/a0022777