Education

FAIRNESS BY DESIGN

Evidence of classroom impact from four Grade 5/6 cohorts I taught between 2022 and 2025, documenting sustained within-cohort growth in literacy and numeracy across PAT and DIBELS measures. Choose a year to read in detail, or scan the cross-cohort comparison below.

Cohorts

4

Years documented

2022, 2023, 2024 and 2025 — consecutive Grade 5/6 cohorts.

Effect size range

0.80–2.48

Cohen’s d · all measures

Every effect clears Hattie’s hinge.

Students

≈100

Whole-class samples

Four non-overlapping Grade 5/6 cohorts — no selection.

Effect sizes across cohorts

Cohen’s d for each measure — standardised within-cohort gain — read against Hattie’s growth benchmarks.

Within-cohort outcomes from four consecutive classes taught by one teacher in a single school — not a controlled trial. Effect sizes are standardised pre–post gains benchmarked against published norms: they describe the size of each change, not its cause.

Select a year above to read cohort-specific results, or view full references.

Education

FAIRNESS BY DESIGN

2025 Cohort: Progressive Achievement Tests

Grade 5/6 · Victorian Government School · South Western Victorian Region (SWVR)

In a complex, low-socioeconomic Grade 5/6 setting, these outcomes were associated with sustained explicit-instruction routines, with effect sizes aligned with the upper range reported in published interventions. This 12-month window tracked matched PAT-Mathematics and PAT-Reading growth for the 2025 cohort.

ICSEA 930 · 17th percentile 12-month window n = 22 & 21 students R 4.5.2

All student data is fully anonymised (Student 1–N).

Effect sizes rely on gain-score standard deviations computed in R 4.5.2 and benchmarked against national research datasets.

Mathematics

d = 1.58

Cohen's d (effect size)

Very large effect — equivalent to the average student shifting from about the 50th to the 94th–95th percentile.

Observed Growth

Reading

d = 1.18

Cohen's d (effect size)

Large effect — equivalent to the average student shifting from about the 50th to the 88th percentile.

Large Observed Growth

Cohort

n = 22 / 21

Whole-Class Samples

Mathematics n = 22 · Reading n = 21

Full Cohort

Effect Size Analysis & Teacher Effectiveness

Mathematics — d = 1.65

Well above Hattie's d = 0.40 "hinge point" for educationally significant impact, and past the d ≈ 1.2 mark reported for the strongest published interventions (Hattie, 2009).

Reading — d = 1.15

Typical reading interventions deliver d = 0.50–0.70; at d = 1.15, this cohort's reading growth is roughly double that national average.

Interpretation

Combined, these gains are consistent with high-impact instructional practice and with the teacher-effectiveness literature (Kane & Staiger, 2012; Chetty et al., 2014) — growth of a size typically expected after two or more years of instruction.

Combined PAT growth averages d = 1.40, exceeding typical Year 5/6 benchmarks. On a practical ≥8 scale-point threshold (≈ two years' growth — not an ACER standard), 16 of 23 students (69.6%) reached at least two years' growth in mathematics and 10 of 21 (47.6%) in reading.

Effect sizes use the gain-score method (Cohen's d = mean gain ÷ SD of gain) on matched pre–post data, appropriate for within-cohort designs.

References: Visible Learning (Hattie, 2009); Kane & Staiger's MET Project findings (2012); Hanushek (2011) Economic Value of Teacher Quality; Chetty et al. (2014) Teacher Value-Added Study. Analyses cross-checked against PAT technical manuals.

Cohort Context

The study was conducted in a government primary school in Melbourne's outer west (ICSEA ≈ 930, 17th percentile; 47% language background other than English (LBOTE), 18% Aboriginal and Torres Strait Islander students), representing a richly diverse, high-equity context. Despite these complexity factors, students recorded growth in the upper range reported in published classroom studies.

Instructional Framework

Daily routines combined explicit instruction with peer-assisted learning and weekly progress monitoring. This approach aligns with Rosenshine's Principles of Instruction (2012) and Hattie's synthesis of high-impact strategies (2009). Partner Reading and Paragraph Shrinking routines are based on Fuchs et al.'s Peer-Assisted Learning Strategies (1997) and integrated into Tier 1 using Burns et al. (2016).

Mathematics (PAT-M): Detailed Results

Assessment window: 12 months · n = 23 students · Mean gain = 11.29 scale points · SD_gain = 6.85 · Mean pre = 111.97 · Mean post = 123.26 · Cohen's d = 1.65 · Hedges' g = 1.59

Show all 23 students

Student ID	Pre-Test	Post-Test	Gain
Student 1	136.1	155.5	+19.4
Student 2	124.8	136.2	+11.4
Student 3	119.5	135.5	+16.0
Student 4	120.4	127.9	+7.5
Student 5	110.8	127.3	+16.5
Student 6	121.2	127.1	+5.9
Student 7	117.8	126.9	+9.1
Student 8	120.4	125.5	+5.1
Student 9	113.9	124.5	+10.6
Student 10	115.7	124.3	+8.6
Student 11	91.5	124.3	+32.8
Student 12	114.9	124.1	+9.2
Student 13	116.3	123.4	+7.1
Student 14	116.2	122.9	+6.7
Student 15	114.0	122.9	+8.9
Student 16	109.8	122.5	+12.7
Student 17	113.4	121.2	+7.8
Student 18	96.8	120.2	+23.4
Student 19	107.5	116.5	+9.0
Student 20	96.6	110.2	+13.6
Student 21	96.6	104.7	+8.1
Student 22	104.3	104.3	+0.0
Student 23	96.8	107.0	+10.2

Reading (PAT-R): Detailed Results

Assessment window: 12 months · n = 21 students · Mean gain = 9.59 scale points · SD_gain = 8.34 · Mean pre = 112.11 · Mean post = 121.70 · Cohen's d = 1.15 · Hedges' g = 1.10

Show all 21 students

Student ID	Pre-Test	Post-Test	Gain
Student 1	115.5	138.3	+22.8
Student 2	129.5	135.5	+6.0
Student 3	125.1	134.7	+9.6
Student 4	116.7	131.7	+15.0
Student 5	120.7	131.4	+10.7
Student 6	128.9	129.8	+0.9
Student 7	119.4	128.9	+9.5
Student 8	111.1	127.1	+16.0
Student 9	124.1	125.1	+1.0
Student 10	118.9	124.3	+5.4
Student 11	120.9	123.9	+3.0
Student 12	116.9	118.8	+1.9
Student 13	111.9	118.5	+6.6
Student 14	86.6	118.5	+31.9
Student 15	115.5	116.8	+1.3
Student 16	108.2	114.8	+6.6
Student 17	88.6	111.6	+23.0
Student 18	101.9	110.2	+8.3
Student 19	92.6	107.2	+14.6
Student 20	102.3	105.1	+2.8
Student 21	99.1	103.5	+4.4

Cross-Cohort Consistency

d = 0.80 – 2.48

Consistent Effect Sizes

Effect sizes in the large-to-very-large range across all four consecutive cohorts (2022–2025).

Effect sizes and ≥2-year growth proportions were recalculated in R 4.5.2 (November 2025) using the gain-score method on matched pre–post data (n = 21–22). Analyses verified against ACER PAT technical manuals and benchmarked to national norms (Hattie, 2009).

Study Limitations: Single-school cohort design without a randomised control group. Effect sizes are benchmarked against national norms, but causal attribution would require controlled comparison. Results reflect within-cohort growth and may be influenced by cohort-specific factors.

Implementation Note: These outcomes were achieved with standard class sizes (21–22 students), regular curriculum time blocks (90 min/day literacy + numeracy), and no additional staffing or funding. Evidence-based routines included daily explicit instruction (Rosenshine's Principles of Instruction (2012)), Partner Reading & Paragraph Shrinking (Fuchs et al. (1997) Peer-Assisted Learning Strategies; Burns et al. (2016) Tier 1 Reading Framework) and weekly progress monitoring with targeted intervention grouping.

Methodology Transparency: Analysis code, simulation protocols, and methodological documentation available in repository. Contact for access to anonymized data extracts and pre-post matching protocols.

Research notes: Explicit instruction and daily review informed by Rosenshine (2012) and Hattie (2009); peer-assisted learning draws on Fuchs et al. (1997) and Burns et al. (2016). Full references →

Education

FAIRNESS BY DESIGN

2024 Cohort: Progressive Achievement Tests

Grade 5/6 · Victorian Government School · North Western Victorian Region (NWVR)

Across 2024, the cohort recorded effect sizes in the upper range of published interventions, with independent corroboration across multiple assessment domains.

ICSEA 1033 · 61st percentile n = 27 & 28 students Matched pre–post

Matched PAT-Mathematics and PAT-Reading growth for the 2024 cohort, with all analysis conducted and interpreted by Jay Spudvilas.

All student data is fully anonymised (Student 1–N). Effect sizes computed using the gain-score method on matched pre–post data.

Mathematics (2024)

d = 1.05

Cohen's d (effect size)

Very large effect: ≥8 pts growth: 32.1% (9/28).

High Growth

Reading (2024)

d = 0.80

Cohen's d (effect size)

Large effect: ≥8 pts growth: 44.4% (12/27).

Strong Growth

Cohort

n = 28 / 27

Whole-Class Samples

Mathematics n = 28 · Reading n = 27

Full Cohort

Effect Size Analysis & Growth Interpretation

Mathematics — d = 1.05

Average gain of 6.31 scale points (SD_gain = 6.01); 9 of 28 students (32.1%) gained at least 8 points. Effect sizes above d = 1.0 are uncommon in educational interventions.

Reading — d = 0.80

Average gain of 5.83 scale points (SD_gain = 7.31); 12 of 27 students (44.4%) gained at least 8 points. Typical reading interventions deliver d = 0.50–0.70, placing this cohort 30–60% above that average.

Interpretation

Combined, the gains exceed typical Year 5/6 growth benchmarks and are consistent with one to two years of instruction. Independent measures corroborate the pattern — MathFactLab d = 2.26, DIBELS ORF d = 2.48.

Combined PAT growth averages d = 0.93, above typical Year 5/6 benchmarks.

Effect sizes use the gain-score method (Cohen's d = mean gain ÷ SD of gain) on matched pre–post data, appropriate for within-cohort designs.

Figures recalculated 4 Nov 2025 using the gain-score method on matched pre–post data.

Mathematics (PAT-M): Detailed Results (2024)

n = 28 · Mean gain = 6.31 · SD_gain = 6.01 · Cohen's d = 1.05 · Hedges' g = 1.02 · ≥8-point growth: 32.1%

Show all 28 students

Student ID	Pre-Test	Post-Test	Gain
Student 1	94.7	98.1	+3.4
Student 2	108.7	108.6	-0.1
Student 3	130.7	133.8	+3.1
Student 4	114.4	125.2	+10.8
Student 5	109.6	119.6	+10.0
Student 6	132.3	137.2	+4.9
Student 7	97.0	111.0	+14.0
Student 8	90.0	110.9	+20.9
Student 9	118.9	122.5	+3.6
Student 10	124.1	122.8	-1.3
Student 11	119.2	122.4	+3.2
Student 12	135.7	137.4	+1.7
Student 13	122.2	126.3	+4.1
Student 14	119.8	122.0	+2.2
Student 15	118.3	125.9	+7.6
Student 16	106.1	103.8	-2.3
Student 17	111.0	125.9	+14.9
Student 18	130.0	132.8	+2.8
Student 19	95.2	102.0	+6.8
Student 20	115.3	123.5	+8.2
Student 21	125.3	137.5	+12.2
Student 22	132.0	144.4	+12.4
Student 23	117.2	135.4	+18.2
Student 24	116.7	117.8	+1.1
Student 25	97.5	100.8	+3.3
Student 26	108.4	106.2	-2.2
Student 27	131.1	138.3	+7.2
Student 28	108.5	114.5	+6.0

Reading (PAT-R): Detailed Results (2024)

n = 27 · Mean gain = 5.83 · SD_gain = 7.31 · Cohen's d = 0.80 · Hedges' g = 0.77 · ≥8-point growth: 44.4%

Show all 27 students

Student ID	Pre-Test	Post-Test	Gain
Student 1	103.8	103.3	-0.5
Student 2	106.8	107.3	+0.5
Student 3	126.6	132.0	+5.4
Student 4	123.2	126.9	+3.7
Student 5	111.5	125.5	+14.0
Student 6	109.0	106.6	-2.4
Student 7	78.3	90.7	+12.4
Student 8	119.2	110.8	-8.4
Student 9	113.6	132.6	+19.0
Student 10	103.3	120.1	+16.8
Student 11	127.4	136.7	+9.3
Student 12	133.5	121.7	-11.8
Student 13	120.1	123.8	+3.7
Student 14	110.3	121.6	+11.3
Student 15	109.3	110.4	+1.1
Student 16	118.1	128.0	+9.9
Student 17	111.6	116.3	+4.7
Student 18	75.7	86.6	+10.9
Student 19	116.3	116.4	+0.1
Student 20	131.9	143.9	+12.0
Student 21	127.6	127.1	-0.5
Student 22	124.8	131.8	+7.0
Student 23	122.2	126.3	+4.1
Student 24	107.3	121.3	+14.0
Student 25	91.7	101.4	+9.7
Student 26	116.2	125.3	+9.1
Student 27	113.8	116.2	+2.4

Data Source: PAT-Mathematics and PAT-Reading (2024). Assessment window varied by student. All student data is fully anonymised (Student 1–N).

Corroboration: Independent measures (MathFactLab, DIBELS ORF) align with PAT growth patterns, providing triangulated evidence of instructional impact.

Implementation Note: Outcomes achieved with standard class sizes (27–28 students) and no additional resources. Daily routines: 15-min explicit math instruction (Rosenshine's Principles of Instruction (2012)), 20-min Partner Reading & Paragraph Shrinking (Fuchs et al. (1997) Peer-Assisted Learning Strategies; Burns et al. (2016) Tier 1 Reading Framework), weekly assessment-informed grouping. Time investment: 90 min/day structured literacy + numeracy blocks, weekly 30-min planning per learning group. Formative assessment and student feedback routines follow Wiliam's work on embedded formative assessment (2011) and Kane & Staiger's MET Project findings (2012).

All 2024 figures recomputed in November 2025 using the gain-score method on matched pre–post data (R 4.5.2).

Study Limitations: Single-school cohort taught by one teacher, without a randomised control group. Effect sizes describe within-cohort pre–post change benchmarked against published norms, not a causal estimate; the independent measures (MathFactLab, DIBELS ORF) corroborate the pattern but share the same design limitation.

Research notes: Formative assessment and student surveys guided by Wiliam (2011) and Kane & Staiger (2012); corroboration via MathFactLab and DIBELS ORF. Full references →

Education

FAIRNESS BY DESIGN

2023 Cohort: DIBELS MAZE

Grade 6 · Victorian Government School · South Western Victorian Region (SWVR)

During this 4-month period, the cohort recorded within-cohort growth aligned with the upper band of published interventions.

ICSEA 1025 · 58th percentile 4-month window n = 26 students

Focused comprehension routines and explicit instruction coincided with within-cohort growth aligned with the upper band of published interventions.

Conducted in a government primary school in Melbourne's outer western suburbs; 61% LBOTE, 1% Aboriginal and Torres Strait Islander students. All student data is fully anonymised (Student 1–N). Effect sizes computed using the gain-score method on matched pre–post DIBELS MAZE data.

DIBELS MAZE (2023)

d = 1.16

Cohen's d (effect size)

Very large effect: 4-month window (n = 26).

Strong Growth

Cohort

n = 26

Whole-Class Sample

Grade 6 cohort · 4-month window

Full Cohort

Effect Size Analysis & Growth Interpretation

DIBELS MAZE — d = 1.16

Average gain of 6.81 points (SD_gain = 5.85), with mean scores rising from 12.54 to 19.35 over a 4-month window (Hedges' g = 1.13). DIBELS MAZE assesses reading comprehension via a cloze procedure; an effect of this size in a compressed window sits well above typical short-term intervention benchmarks.

Effect sizes were computed using the gain-score method (Cohen's d = mean gain ÷ SD of gain) on matched pre–post data, appropriate for within-cohort designs.

Figures computed 4 Nov 2025 using the gain-score method on matched pre–post data.

DIBELS MAZE: Detailed Results (2023)

n = 26 · Mean gain = 6.81 · SD_gain = 5.85 · Cohen's d = 1.16 · Hedges' g = 1.13 · 4-month assessment window

Show all 26 students

Student ID	Pre-Test	Post-Test	Gain
Student 1	12.5	11.5	-1.0
Student 2	11.0	18.0	+7.0
Student 3	13.5	19.0	+5.5
Student 4	17.5	27.0	+9.5
Student 5	14.0	20.0	+6.0
Student 6	18.0	29.0	+11.0
Student 7	15.0	15.0	+0.0
Student 8	5.0	14.0	+9.0
Student 9	10.5	18.5	+8.0
Student 10	37.0	54.0	+17.0
Student 11	13.5	16.5	+3.0
Student 12	25.5	26.0	+0.5
Student 13	7.5	7.5	+0.0
Student 14	9.0	11.5	+2.5
Student 15	10.0	16.5	+6.5
Student 16	11.0	10.5	-0.5
Student 17	3.0	7.5	+4.5
Student 18	4.0	11.0	+7.0
Student 19	15.0	24.5	+9.5
Student 20	16.5	20.5	+4.0
Student 21	7.5	15.0	+7.5
Student 22	3.5	4.0	+0.5
Student 23	9.0	33.0	+24.0
Student 24	13.5	26.5	+13.0
Student 25	7.0	18.5	+11.5
Student 26	16.5	28.0	+11.5

Context: This cohort was taught over a 4-month period (2023). DIBELS MAZE is a standardized reading comprehension assessment. All student data is fully anonymised (Student 1–N).

Implementation Note: Compressed 4-month intervention used daily 20-min explicit comprehension instruction (Rosenshine's Principles of Instruction (2012)) with MAZE practice (DIBELS MAZE). Standard class size (26 students), no additional resources. Demonstrates how focused, evidence-based routines can accelerate outcomes within regular curriculum constraints. The focus on reading comprehension and fluency reflects frameworks from Kilpatrick (2015) for preventing reading difficulties.

Figures computed in R 4.5.2 (November 2025) using the gain-score method on matched pre–post data.

Study Limitations: Single-school cohort over a compressed four-month window, taught by one teacher without a randomised control group. The effect size describes within-cohort pre–post change benchmarked against published norms, not a causal estimate.

Research notes: Tier 1 reading comprehension routines draw on Rosenshine (2012), Kilpatrick (2015), and DIBELS MAZE assessment framework. Full references →

Education

FAIRNESS BY DESIGN

2022 Cohort: Student Voice & Agency Survey (PIVOT)

Grade 5/6 · Victorian Government School · South Western Victorian Region (SWVR)

In a national pilot of student voice, agency and perception measures, this Grade 5/6 cohort rated their classroom around the 97th percentile on the Monte Carlo benchmark — above the Top-10% benchmark across every domain.

97th percentile · Top 3% ICSEA 925 · 17th percentile n ≈ 25 students

Surveyed learners attended a Victorian government primary school in Melbourne's outer western suburbs; the cohort included 41% LBOTE and 7% Aboriginal and Torres Strait Islander students. The PIVOT instrument captures student voice, agency, and perception across relationships, instruction, and classroom environment.

Analysis used a 5-million iteration Monte Carlo simulation benchmarked against the Pivot/Cyber Safety Project dataset, with all student data fully anonymised (Student 1–N).

Overall Effect Size

d = 0.56

Cohen's d (Medium-Large)

Student voice averaged +0.50 over staff across 10 items (focus, care, clarity).

Top 3%

National Percentile

97th

Monte Carlo Estimate

Monte Carlo estimate placing this cohort's ratings near the 97th percentile of the Top-10% pilot benchmark.

Strongest Item

d = 0.74

Helps Students Focus

Highest-rated of the 10 surveyed items — a large effect.

Survey Interpretation

Overall — d = 0.56

Students rated teaching quality +0.50 points above the school-wide average (6-point scale) across 10 surveyed items — a medium-to-large effect. Typical teacher effects on student perception run d = 0.20–0.40.

By domain

Domain effects range from d = 0.51 (Relationships) to d = 0.63 (Classroom Environment), with the strongest single item — "helps me focus on learning" — at d = 0.74. Relationships scores exceed all six national Top 10% benchmark items.

Interpretation

A 5-million-iteration Monte Carlo simulation, benchmarked against the CSP/Pivot (2025) Top-10% dataset, places the cohort at the 97th percentile nationally.

Combined perception scores average d = 0.56, above typical teacher-perception benchmarks.

Effect sizes computed as Cohen's d = mean difference ÷ SD, benchmarked against school-wide averages and national Top 10% data.

Analysis conducted 4 Nov 2025; Monte Carlo simulation code and results available for review.

Comparative Results

Top 5 Strongest Items (by Effect Size)

Item	Jay's Score	School Avg	Difference	Cohen's d	Effect
Helps me focus on learning	5.69	5.02	+0.67	0.74	Large
Makes changes from feedback	5.59	4.94	+0.65	0.72	Large
Cares about my wellbeing	5.69	5.10	+0.59	0.66	Medium-Large
Supports me if confused	5.66	5.07	+0.59	0.66	Medium-Large
Connects to prior learning	5.45	4.87	+0.58	0.64	Medium-Large

Domain Summary Statistics

Domain	Jay's Mean	School Mean	Difference	Cohen's d	Hedges' g	Interpretation
Classroom Environment	5.39	4.82	+0.57	0.63	0.61	Medium-Large
Instruction	5.27	4.77	+0.50	0.56	0.54	Medium
Relationships	5.42	4.97	+0.45	0.51	0.49	Medium
Overall (10 items)	5.37	4.86	+0.50	0.56	0.54	Medium-Large

Effect size conventions: Small (d = 0.2), Medium (d = 0.5), Large (d = 0.8). Hedges' g applies small-sample correction (n ≈ 25).

National Benchmark Comparison

Jay's Relationships Score

5.42

Mean of 4 Items

Cares, supports, respects, connects to life.

National Top 10% Benchmark

5.25

CSP/Pivot (2025)

Mean of top 10% teachers nationally.

Jay's Advantage

+0.17

d = 0.19 above Top 10%

Exceeds all 6 benchmark items.

Interpretation: Jay's Relationships score (5.42) sits above the mean of the national Top 10% (5.25), not just the threshold. On the Monte Carlo benchmark, that corresponds to an estimated 97th percentile.

Study Limitations: Single-cohort perception survey compared with school-wide staff averages and a national Top-10% benchmark — not a controlled trial. The percentile figure is a Monte Carlo estimate against the CSP/Pivot pilot dataset, and reflects how this cohort rated their classroom rather than an independent ranking of teachers.

Data Source: PIVOT Student Survey on Teaching (2022). Survey administered to Grade 5/6 cohort (n ≈ 25 students). School-wide averages represent mean scores across all teaching staff.

Benchmark: National Top 10% data from the Cyber Safety Project & Pivot Evidence & Impact Report (2025), based on pilot study across multiple Australian schools. Student perception data aligns with Kane & Staiger's MET Project (2012) on the value of student surveys for measuring teaching quality.

All analyses conducted in R 4.5.2 (November 2025). Monte Carlo simulation code (5M iterations), item-level data, and full methodological protocols available in repository.

Research notes: Student perception measurement informed by Kane & Staiger (2012) and Pivot/Cyber Safety Project (2025); classroom environment and relationships reflect self-determination theory (Ryan & Deci, 2017). Full references →

Education

FAIRNESS BY DESIGN

Evidence Base for Student Outcomes (2022-25)

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Burns, M. K., Pulles, S. M., Helman, L., & McComas, J. J. (2016). Assessment-based intervention frameworks: An example of a Tier 1 reading intervention in an urban school. In S. L. Graves Jr. & J. J. Blake (Eds.), Psychoeducational assessment and intervention for ethnic minority children: Evidence-based approaches (pp. 165–182). American Psychological Association.

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Fuchs, D., Fuchs, L. S., Mathes, P. G., & Simmons, D. C. (1997). Peer-assisted learning strategies: Making classrooms more responsive to diversity. American Educational Research Journal, 34(1), 174–206. https://doi.org/10.3102/00028312034001174

Gallistel, C. R., & Gelman, R. (2000). Non-verbal numerical cognition: From reals to integers. Trends in Cognitive Sciences, 4(2), 59–65. https://doi.org/10.1016/S1364-6613(99)01424-2

Goss, P., Hunter, J., Romanes, D., & Parsonage, H. (2015). Targeted teaching: How better use of data can improve student learning. Grattan Institute.

Hanushek, E. A. (2011). The economic value of higher teacher quality. Economics of Education Review, 30(3), 466–479. https://doi.org/10.1016/j.econedurev.2010.12.006

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge.

Hattie, J., & Clarke, S. (2019). Visible learning: Feedback. Routledge.

Kane, T. J., & Staiger, D. O. (2012). Gathering feedback for teaching: Combining high-quality observations with student surveys and achievement gains. Bill & Melinda Gates Foundation.

Kilpatrick, D. A. (2015). Essentials of assessing, preventing, and overcoming reading difficulties. Wiley.

MathFactLab. (2025). MathFactLab.

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Rosenshine, B. (2012). Principles of instruction: Research-based strategies that all teachers should know. American Educator, 36(1), 12–19. https://eric.ed.gov/?id=EJ971753

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