AV communication review

In recent years, online learning has become more and more popular. However, because of information overload, learners often find it difficult to retrieve suitable learning resources. Although many scholars have proposed excellent online learning resource recommendation algorithms, the accuracy of personalized recommendation results still needs to be improved. This study proposes an improved hybrid ontology-based approach for online learning resource recommendations, combining collaborative filtering algorithm and sequential pattern mining (SPM) techniques. Ontology can be used effectively for knowledge representation to avoid cold start and data sparsity problems. And the history of learners’ sequential access patterns helps in providing recommendations that are more consistent with the law of learning activities. Experimental results reveal that our improved hybrid approach for learning resource recommendations yields better performance and recommendation quality than other related algorithms. Compared with previous research outcomes, our collaborative filtering engine, with ontology domain knowledge, makes full use of the historical learning paths of similar learners. The ontology construction in this study has a more reliable theoretical basis and the selection of features is more representative. In addition, improvement of the SPM process further improves the efficiency of our recommended algorithm.

The purpose of this study was to develop a mobile learning acceptance model for pre-service teachers and to examine the relationships among technology acceptance factors. The literature on mobile learning acceptance lacks studies on pre-service teachers and studies that include concrete mobile learning scenarios. To overcome these problems, we have developed and implemented a mobile-technology-enabled information technology course. The data collection and analysis were conducted in two separate studies. First, we developed a mobile learning acceptance scale and applied confirmatory factor analysis with 408 participants. The final instrument included 28 items measuring eight technology acceptance factors, namely behavioral intention, attitude towards use, perceived usefulness, perceived ease of use, social influence, facilitating conditions, self-efficacy, and anxiety. After this, we collected a new set of data from 316 participants to examine the relationships among the factors using structural equation modeling. In both studies, we investigated the respective models’ invariance across gender and discipline groups, and both models fulfilled invariance requirements. The results indicated that perceived ease of use and social influence have direct effects on behavioral intention, whereas self-efficacy has an indirect effect. Depending on the group, the explained variance of behavioral intention ranged between 18.1% and 60.6%.

The following empirical study examines the acquisition of expertise. The model structures in the domain of empirical methodologies were examined in a time period of two semesters. At each measurement point (N = 5), the model structures of the reference groups were compared with those of the group of learners. The group of learners consisted of (N = 31) students who did not have any experience with research methods and their theoretical foundations. The results indicate that the external criteria enabled a precise assessment of learning dependent model change.

This study assessed the role different kinds of secondary tasks play for researching the modality effect of cognitive load theory. Ninety-six university students worked with a computer-based training program for approximately 13 min and had to fulfill an additional secondary task. In a 2 × 2 factorial design, modality of information presentation (within factor) and design of secondary task (between factor) were varied. Students of both experimental groups learned with visual-only and audiovisual information presentation. The secondary task consisted of monitoring an object either displayed spatially contiguous (monitoring the screen background color, N = 46) or spatially non-contiguous (monitoring a letter color in the upper part of the screen, N = 50). Reaction times on this secondary task were used to measure cognitive load. Results show that the modality effect only appears with the spatially non-contiguous task but not with the spatially contiguous task. We interpret this effect as due to only partial utilization of working memory capacity by the combination of primary task and spatially contiguous secondary task. The results highlight the importance of an appropriate secondary task design when investigating the modality effect but also not to overgeneralize multimedia design guidelines.

Understanding experiences in online educational settings is crucial to improving teaching and learning. The purpose of this paper is to describe Narrative Inquiry as a research methodology that has the potential create the relational opportunities necessary to understand experiences in online learning environments. In this article, I use an example from research conducted in a special educational setting to overview narrative inquiry methodology, explain its theoretical underpinnings, and highlight its potential to enhance current knowledge of how individuals live alongside one another in online educational settings. I will also explain how narrative inquiry can support the development of new insights about time and engagement in online learning. Then I address how narrative inquiry has the potential to advance equitable research practices in these settings. Finally, I offer suggestions for future research projects that leverage the conceptual strengths and methodological tools of narrative inquiry.