Effects of Progress Monitoring on Math Performance of At-Risk Students

Lambert, Richard; Algozzine, Bob; McGee, Jennifer

doi:10.9734/bjesbs/2014/7259

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Additional studies have been conducted using AM in math classes (e.g. Gaeddert, ; Lambert, Algozzine & Mc Gee, ; Powell, ; Ysseldyke, Spicuzza, Kosciolek & Boys, ), which have similarly shown positive effects on students' math achievement.…”

Section: Computer‐based Assessment For Learningmentioning

confidence: 99%

“…Burns et al, ). Thus, the second takeaway message is that the duration of using CBAfL systems like AM influences results – that is, the more students use such systems, the better they perform on end‐of‐year standardized tests (Burns et al, ; Gaeddert, ; Lambert et al, ; Powell, ; Ysseldyke et al, ). Next, we discuss the literature related to Web‐based CBAfL.…”

Section: Computer‐based Assessment For Learningmentioning

confidence: 99%

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Review of computer‐based assessment for learning in elementary and secondary education

Shute

Rahimi

2017

Computer Assisted Learning

172

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In this paper, we review computer-based assessment for learning (CBAfL), in elementary and secondary education, as a viable way to merge instruction and assessment of students' developing proficiencies. We begin by contextualizing our topic relative to summative and formative assessment before presenting the current literature, which we categorized into the following: (a) supplementary use in classrooms, (b) web-based, and (c) data-driven, continuous CBAfL. Examples of research studies per category are provided. Findings show that using CBAfL in the classroom, via the Internet, or embedded in a game, generally enhances learning and other outcomes across a range of content areas (e.g. biology, math, and programming). One conclusion is that feedback, to be most beneficial to learning, should not be overly complex and must be used to be effective. Findings also showed that the quality of the assessment (i.e. validity, reliability, and efficiency) is unimpaired by the inclusion of feedback. The possibilities created by advances in the learning sciences, measurement, and technology have paved the way toward new assessment approaches that will support personalized learning and that can accurately measure and support complex competencies. The next steps involve evaluating the new assessments regarding their psychometric properties and support of learning. KeywordsAssessment, learning, assessment for learning, computer-based assessment for learning, elementary education, secondary education.Assessment should not merely be done to students; rather, it should also be done for students, to guide and enhance their learning (National Council of Teachers of Mathematics, 2000).

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Section: Computer‐based Assessment For Learningmentioning

confidence: 99%

Section: Computer‐based Assessment For Learningmentioning

confidence: 99%

Review of computer‐based assessment for learning in elementary and secondary education

Shute

Rahimi

2017

Computer Assisted Learning

172

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“…Star assessments are well established as having strong psychometric properties. Moreover, considerable evidence in support of their use as progress monitoring measures has also been documented in the literature (e.g., [40][41][42]). The purpose of this study was to extend the existing evidence to identify an optimal methodology that could maximize the potential of using multiple Star assessment scores to produce highly accurate screening data for students at risk for mathematics difficulties later in the school year.…”

Section: Discussionmentioning

confidence: 99%

Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach

2022

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Traditional screening approaches identify students who might be at risk for academic problems based on how they perform on a single screening measure. However, using multiple screening measures may improve accuracy when identifying at-risk students. The advent of machine learning algorithms has allowed researchers to consider using advanced predictive models to identify at-risk students. The purpose of this study is to investigate if machine learning algorithms can strengthen the accuracy of predictions made from progress monitoring data to classify students as at risk for low mathematics performance. This study used a sample of first-grade students who completed a series of computerized formative assessments (Star Math, Star Reading, and Star Early Literacy) during the 2016–2017 (n = 45,478) and 2017–2018 (n = 45,501) school years. Predictive models using two machine learning algorithms (i.e., Random Forest and LogitBoost) were constructed to identify students at risk for low mathematics performance. The classification results were evaluated using evaluation metrics of accuracy, sensitivity, specificity, F1, and Matthews correlation coefficient. Across the five metrics, a multi-measure screening procedure involving mathematics, reading, and early literacy scores generally outperformed single-measure approaches relying solely on mathematics scores. These findings suggest that educators may be able to use a cluster of measures administered once at the beginning of the school year to screen their first grade for at-risk math performance.

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“…There was a strong correlation between student performance and amount of time for AM. Lambert, Algozzine, and McGee (2014) categorized AM treatment classes in grades 2-5 at three Midwestern elementary schools into high-or low-implementation groups. Growth for the high-implementation group was significantly higher than for the low-implementation group, although both groups did better than non-AM controls.…”

Section: Outcome and Implementation Fidelity In Accelerated Mathsmentioning

confidence: 99%

Implementation fidelity and attainment in computerized practice of mathematics

Topping

2019

Research Papers in Education

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Measuring the implementation fidelity (IF) or integrity of interventions is crucial, otherwise a positive or negative outcome cannot be interpreted. Direct and indirect methods of IF measurement tend to over-emphasize teacher behaviour. This paper focuses on IF measured by student behaviour collected through computers. Attainment was measured by the STAR test of maths (a computerized item-banked adaptive norm-referenced test). Implementation quality (IF) was measured by Accelerated Maths (AM) (an instruction-free personalized practice and progress-monitoring system in mastery of mathematics skills). Attainment data was gathered in the UK on 20,103 students in 148 schools, and of these implementation data on n=6,285. Only a small percentage of pupils scored on five AM implementation indices at or above the levels recommended. Correlations between attainment and implementation indices were modest, but high implementation was positively correlated with high attainment. Socioeconomic status did not appear to affect implementation or attainment. Implementation quality of AM is clearly a problem in the UK, and needs improvement. However, overall students still scored above average on attainment.Length: 7399 words 28 pages excluding tables (449 words, 4 pages) Running Head: IMPLEMENTATION FIDELITY IN COMPUTERIZED MATHS

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Effects of Progress Monitoring on Math Performance of At-Risk Students

Cited by 4 publications

References 20 publications

Review of computer‐based assessment for learning in elementary and secondary education

Review of computer‐based assessment for learning in elementary and secondary education

Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach

Implementation fidelity and attainment in computerized practice of mathematics

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