Effectiveness of Computer-Assisted Instruction in Statistics

Abstract

Although previous meta-analyses have documented the efficacy of computerassisted statistics instruction, the current study examined a range of specific features that presumably influence its effectiveness, such as the level of learner engagement, learner control, and the nature of feedback. In 45 experimental studies with a control condition, computer-assisted statistics instruction provided a meaningful average performance advantage (d = 0.33). Because of great methodological heterogeneity among the studies, the authors employed a conservative but appropriate mixed effects model to examine potential moderator effects. The authors' analyses revealed three statistically significant findings. Larger effects were reported in studies in which treatment groups received more instructional time than control groups, in studies that recruited graduate students as participants, and in studies employing an embedded assessment. A newly developed second order standardized mean effect size, d diff , reveals that additional study characteristics may serve as meaningful moderators. Tight experimental control is needed to assess the importance of specific instructional features in computer-assisted statistics instruction.

Pronunciation Barriers and Computer Assisted Language Learning (CALL) Coping the Demands of 21st Century in Second Language Learning Classroom in Pakistan

“…The findings of the studies indicated that CALL-laden approaches proved beneficial for language and pronunciation teaching and these sorts of activities enhanced students' skill and performance to a greater degree. Interactive technology can enable students to organize information so that it can be connected to their prior knowledge or existing schemata (Sosa, Berger, & Mary, 2011). Lambacher (1999) used pronunciation software to forty primary school Japanese learners.…”

Simulating seasonal concentration in tourism series

“…Simulating and experimenting with data in an interactive manner is thought to be a more effective way to assimilate and understand complex and abstract concepts than using traditional methods in the classroom (Sosa, Berger, Saw, & Mary, 2011;Gonzaĺez, Jover, Cobo, & Munõz, 2010;Dinov, Christou, & Sanchez, 2008;Lunsford, Rowell, & Goodson-Espy, 2006). It is expected that these interactive electronic resources do the calculations in the background and show the visual results interactively, thus allowing students to focus on exploring and investigating simulated or real-life examples (Bolliger, & Supanakorn, 2011;Chance, Ben-Zvi, Garfield, & Medina, 2007), as well as in geovisualisation (Sigala, 2013).…”

Application of a Discrete-time Markov Chain Simulation in Insurance

“…In recent years, there have been many attempts to overcome this problem in statistics education generally using interactive learning tools. It has been widely recognized that the effectiveness of the learning process is enhanced by means of experimenting and simulating, usually with digital tools [1], [2], [3], [4].…”