Collaborative Video Viewing (CVV) transforms passive video-based learning into an engaging, active process. While collaborative modes have different affordances that could potentially influence knowledge co-construction, no study has directly assessed the impact of collaborative modes in CVV activities. Therefore, this current study seeks to investigate how collaborative modes influence learning outcomes, learning engagement, group interaction and the co-construction process.The study utilized a within-subject, counterbalanced experimental design, in which each participating undergraduate student was paired in dyads. These dyads were assigned to engage in two separate CVV sessions: one using synchronous voice-based collaborative mode (SV) and the other using asynchronous text-based collaborative mode (AT). After each session, participants completed a test consisting of retention and application questions. ANCOVA was utilized to analyze the test scores. To ascertain if the different scores were a result of varying levels of learning engagement, dyad discussions were coded using ICAP coding (Chi & Wylie, 2014). Furthermore, to delve deeper into the group interaction mechanism in SV and AT, a codebook was developed to analyze the discourse that occurred during dyad interaction. Sequential analysis and thematic narrative analysis were employed to visualize interaction patterns and the co-construction process. The findings indicated that, generally, SV dyads performed better on application scores and have significantly higher interactive learning engagement than AT dyads. In line with ICAP predictions, the higher-score groups in both SV and AT engaged in more generative processes, leading to more constructive and interactive comments than lower-scoring groups. In terms of group interaction, both SV and AT primarily use descriptive discourse for co-explanation. However, the SV groups exclusively introduce discourse expressing uncertainty, which subsequently leads to group negotiation. The study identified distinct knowledge co-construction phases, including (a) co-explanation, (b) negotiation, and (c) application. Although the co-explanation phase is the most frequent in all dyad scores in both SV and AT, the negotiation phase appears to differentiate low-high score dyads from high-high score dyads. These findings hold research implications for understanding learning engagement and group interaction in various online collaborative modes, as well as for the instructional design of active video-based learning through collaborative video viewing.

The mathematics test is the most difficult test in the GED (General Education Development) Test battery, largely due to the presence of story problems. Raising performance levels of story problem-solving would have a significant effect on GED Test passage rates. The subject of this formative research study is Ms. Stephens’ Categorization Practice Utility (MS-CPU), an example-tracing intelligent tutoring system that serves as practice for the first step (problem categorization) in a larger comprehensive story problem-solving pedagogy that purports to raise the level of story problem-solving performance. During the analysis phase of this project, knowledge components and particular competencies that enable learning (schema building) were identified. During the development phase, a tutoring system was designed and implemented that algorithmically teaches these competencies to the student with graphical, interactive, and animated utilities. Because the tutoring system provides a much more concrete rather than conceptual, learning environment, it should foster a much greater apprehension of a story problem-solving process. With this experience, the student should begin to recognize the generalizability of concrete operations that accomplish particular story problem-solving goals and begin to build conceptual knowledge and a more conceptual approach to the task. During the formative evaluation phase, qualitative methods were used to identify obstacles in the MS-CPU user interface and disconnections in the pedagogy that impede learning story problem categorization and solution preparation. The study was conducted over two iterations where identification of obstacles and change plans (mitigations) produced a qualitative data table used to modify the first version systems (MS-CPU 1.1). Mitigation corrections produced the second version of the MS-CPU 1.2, and the next iteration of the study was conducted producing a second set of obstacle/mitigation tables. Pre-posttests were conducted in each iteration to provide corroboration for the effectiveness of the mitigations that were performed. The study resulted in the identification of a number of learning obstacles in the first version of the MS-CPU 1.1. Their mitigation produced a second version of the MS-CPU 1.2 whose identified obstacles were much less than the first version. It was determined that an additional iteration is needed before more quantitative research is conducted.