This meta-analysis extended the current literature regarding the effects of computer technology (CT) on mathematics achievement, with a particular focus on low-performing students. A total of 45 independent effect sizes extracted from 31 empirical studies based on a total of 2,044 low-performing students in K-12 classrooms were included in this meta-analysis. Consistent with previous reviews, this study suggested a statistically significant and positive effect of CT ([Formula: see text] = 0.56) on low-performing students’ mathematics achievement. Of four CT types, the largest CT effect was found with problem-solving system ([Formula: see text] = 0.86), followed by tutoring [Formula: see text] = 0.80), game-based intervention ([Formula: see text] = .58), and computerized practice ([Formula: see text] = .23). Furthermore, other moderators were found to explain variation in CT effects on low-performing students’ mathematics achievement. Study findings contributed to clarifying the effect of CT for low-performing students’ mathematics achievement. Implications for instructional design and practices are also discussed.

Response time information has recently attracted significant attention in the literature as it may provide meaningful information about item preknowledge. The methods that use response time information to identify examinees with potential item preknowledge make an implicit assumption that the examinees with item preknowledge differ in their response time patterns when compared to examinees without item preknowledge. This study uses a multiple‐group extension of van der Linden's Lognormal Response Time Model with a gated mechanism to quantify the differences in latent speed between examinees with and without item preknowledge when responding to compromised and uncompromised items. Our findings indicate that there may be a wide range for potential effect of item preknowledge on response time. The effect of item preknowledge on response times is likely to be contextual and depends on many factors such as characteristics of test‐takers, the nature of item preknowledge, and item characteristics. We recommend that researchers systematically quantify the effect sizes in their studies, and if possible, manipulate the effect size for a wide range while studying the effectiveness of already existing or new methods to detect examinees with item preknowledge through simulation.

Response time (RT) information has recently attracted a significant amount of attention in the literature as it may provide meaningful information about item preknowledge. In this study, a Deterministic Gated Lognormal Response Time (DG-LNRT) model is proposed to identify examinees with potential item preknowledge using RT information. The proposed model is applied to a real experimental dataset provided by Toton and Maynes (2019) in which item preknowledge was manipulated, and its performance is demonstrated. Then, the performance of the DG-LNRT model is investigated through a simulation study. The model is estimated using the Bayesian framework via Stan. The results indicate that the proposed model is viable and has the potential to be useful in detecting cheating by using response time differences between compromised and uncompromised items.

Response time information has recently attracted a significant amount of attention in the literature as it may provide meaningful information about item preknowledge. The methods that propose the use of response time information in identifying examinees with potential item preknowledge make an implicit assumption that the examinees with item preknowledge differ in their response time patterns compared to other examinees without item preknowledge. In this study, we analyzed the differences in response time of examinees with potential item preknowledge and examinees without item preknowledge based on a real experimental dataset provided by Toton and Maynes (2019). A multiple-group extension of van der Linden’s Lognormal Response Model with a gating mechanism was used to capture the differences in latent speed for control and experimental groups on disclosed and undisclosed items. The model used in the study and estimated parameters from this experimental dataset may inform future simulation studies in this area of research to simulate realistic datasets with item preknowledge behavior.

Response time (RT) information has recently attracted a significant amount of attention in the literature as it may provide meaningful information about item preknowledge. In this study, a Deterministic Gated Lognormal Response Time (DG-LNRT) model is proposed to identify examinees with potential item preknowledge using RT information. The proposed model is applied to a real experimental dataset provided by Toton and Maynes (2019) in which item preknowledge was manipulated, and its performance is demonstrated. Then, the performance of the DG-LNRT model is investigated through a simulation study. The model is estimated using the Bayesian framework via Stan. The results indicate that the proposed model is viable and has the potential to be useful in detecting cheating by using response time differences between compromised and uncompromised items.