Teomara Rutherford

A Randomized Trial of an Elementary School Mathematics Software Intervention: Spatial-Temporal Math.

Abstract Fifty-two low performing schools were randomly assigned to receive Spatial-Temporal (ST) Math, a supplemental mathematics software and instructional program, in second/third or fourth/fifth grades or to a business-as-usual control. Analyses reveal a negligible effect of ST Math on mathematics scores, which did not differ significantly across subgroups defined by prior math proficiency and English Language Learner status. Two years of program treatment produced a nonsignificant effect. Publication of evaluation results from large-scale real-world supplemental mathematics instructional implementations such as this one can provide a realistic view of the possibilities, costs, and limitations of this and other computer aided instruction supplemental interventions.

The effects of mathematics instruction using spatial temporal cognition on teacher efficacy and instructional practices

This paper examined the effects of an instructional approach known as Spatial Temporal Mathematics (ST Math) on teacher beliefs about mathematics teaching. Participants were 339 elementary teachers teaching grades 2-5 who were randomly assigned to a control or treatment group. Hierarchical linear modeling was used to determine the effects of the intervention on self-efficacy, outcome expectancy, and instructional practices using scientific reasoning. While the treatment did not yield significant effects in teacher outcomes, our secondary analysis indicated that time on ST Math and the integration of ST Math into daily instructions were positively associated with teacher efficacy and instructional practices using scientific reasoning. Implications of the results on teacher beliefs about mathematics teaching are discussed.

Construct Confounding Among Predictors of Mathematics Achievement

Identifying which early mathematics skills have the largest effects on later mathematics achievement has important implications. However, regression-based estimates often rely on untested assumptions: (a) Scores on different mathematics skills reflect unique constructs, and (b) other factors affecting early and later mathematics achievement are fully controlled. We illustrate a process to test these assumptions with a sample of third and fourth graders who completed measures of mathematics skills, working memory and motivation, and standardized mathematics and English language arts tests. Factor analyses indicated that mathematics skills largely reflect the same underlying construct. The skills that loaded highest on the general factor most predicted both later mathematics and English language arts, even after adjusting for working memory and motivation. Findings suggest that relations between earlier mathematics and later achievement largely reflected more general factors that contribute to children’s learning. We discuss the importance of establishing construct validity in correlational studies.

Using Serious Game Analytics to Inform Digital Curricular Sequencing: What Math Objective Should Students Play Next?

This paper applied serious game analytics to inform digital curricular sequencing in a longitude, curriculum-integrated math game, ST Math. When integrating serious games into classrooms, teachers may have the flexibility to change the order of math objectives for student groups to play. However, it is unclear how teacher decisions, as well as the sequencing of the original curricular order affect students. Moreover, few researchers have applied data-driven methods to inform content ordering in educational games, where the nature of educational content and student behaviors are different from many e-learning platforms. In this paper, we present a novel method that suggests curricular sequencing based on the prediction relationship between math objectives. Our results include specific design recommendations for ST Math, and general data-driven insights for digital curricular design, such as the pacing of objectives and the ordering of math concepts. Our method can potentially be applied to data from a wide range of games and digital learning platforms, enabling developers to better understand how to sequence educational content.

Learning Curve Analysis in a Large-Scale, Drill-and-Practice Serious Math Game: Where Is Learning Support Needed?

This paper applies data-driven methods to understand learning and derives game design insights in a large-scale, drill-and-practice game: Spatial Temporal (ST) Math. In order for serious games to thrive we must develop efficient, scalable methods to evaluate games against their educational goals. Learning models have matured in recent years and have been applied across e-learning platforms but they have not been used widely in serious games. We applied empirical learning curve analyses to ST Math under different assumptions of how knowledge components are defined in the game and map to game contents. We derived actionable game design feedback and educational insights regarding fraction learning. Our results revealed cases where students failed to transfer knowledge between math skills, content, and problem representations. This work stresses the importance of designing games that support students’ comprehension of math concepts, rather than the learning of content- and situation-specific skills to pass games.

Applying Self-regulated Learning to the Dynamic STEM Classroom

The current chapter uses the Pintrich social cognitive model of self-regulated learning to analyze the self-regulatory processes evident in an integrative design and engineering high school classroom. In particular, the analysis focuses on the evidence that the teacher creates an environment rich for both teacher and student self-regulation, and on how he leverages this environment to model and scaffold self-regulated learning. Three areas of strength in the classroom with respect to SRL are highlighted: (1) the motivational climate within the classroom and the motivational affordances of the curriculum, (2) the teachers’ practices regarding assessment and evaluation, and (3) the modeling and scaffolding of SRL-supportive skills inherent in the structure of and activities within the course. Analyses reveal the importance of the teacher’s own regulatory process as well as the use of the social context in the scaffolding and modelling of self-regulatory processes. Recommendations for practitioners and future research are discussed.

Blood from a stone: Where teachers report finding time for computer-based instruction

Abstract In an era of accountability, implementation of computer-based instruction (CBI) may be attractive to personalize learning and raise test scores. Although prior work has examined how CBI may serve these purposes, we demonstrate that it also has an unintentional impact on how teachers allocate time in the classroom. The purpose of this study was to investigate the potential collateral damage of implementing CBI in elementary classrooms. Using the implementation of the supplemental CBI, Spatial Temporal (ST) Math as a case study, we analyze how 468 teachers report spending instructional minutes in each subject per week, and which subjects are reported as being limited to accommodate the program. Results reveal teachers vary in time spent across subjects and from where they draw time for ST Math. Most time for ST Math comes from a reduction of non-math core subject time with some variation according to grade. We provide initial evidence of teacher autonomy in subject time allocation within the context of CBI implementation; further study is needed to illuminate drivers of teachers decisions.