21st century assessment : an examination of the relationship among computer-adaptive homework, self-regulation strategies and student scores on computer-adaptive assessment

Abstract

This study investigated the relationship between fifth grade students' computer-adaptive assessment performance (TAP) scores when a computer-adaptive eLearning platform was assigned for homework along with a self-regulated learning (SRL) treatment intervention. The adaptive learning theoretical model and the TAP conceptual framework supported the rationale for the utilization of the digital computer-adaptive systems of learning and assessing. In addition, the study examined the predictive ability of the Measures Strategy Learning Questionnaire (MSLQ) a self-reporting survey on MAP assessment performance scores. The theory of SRL provided the foundation for the strategies implemented in this study. The participants consisted of three fifth-grade classes split across two elementary schools within a single school district in the Southwest suburbs of Chicago. One school housed 41 of the participants in two classrooms. In Class One, there was a total of 19 participants, and Class Two consisted of a total of 22 participants. These students were assigned the computer-adaptive English Language Arts (ELA) homework, while the control group of 17 participants attended the second school and were assigned traditional homework (e.g., pencil and paper assignments). Random assignment of the participants was not possible since the fourth grade teachers had equally distributed the students by race, gender, academic ability, and behavior at the end of previous 2014-2105 school year. The three classes were comparable in terms of participants' gender, racial/ethnic identity, and socioeconomic status. In lieu of assigning students to classes, conditions were assigned to the three participating classes. In total, 58 students participated in this study. The findings of this study showed the scores on the Measures of Academic Progress assessment (MAP) increased significantly from pretest to posttest across all conditions. There were no statistical significant differences in posttest MAP composite scores based on the treatment conditions. The students who participated in computer-adaptive homework with a self-regulated learning strategy treatment intervention did not perform significantly better on the MAP than students using computer-adaptive homework only. Equally, students who used computer-adaptive homework did not perform significantly better than students in the control group. Finally, scores on the MSLQ did not predict students' performance on the posttest MAP composite score. Although the findings of the current study lacked statistical significance, the findings provided additional research in the areas of computer-adaptive platforms used for homework assignments, the implementation of self-regulated learning strategies, and the impact on computer-adaptive assessment. Considering the recent advancements in educational technology and implementation of the new generation of digital assessments, the findings support additional research needs to be done to identify the specific ways in which computer-adaptive eLearning platforms can support student performance and academic success on the new digital assessments.