LMS DESIGN INTERVENTIONS FORENHANCING THE INTENTION TO CONTINUE USE

Learners, according to the literature, believe that the use of a Learning Management System increases self-regulated behaviour, but even so, a significant number of them have no positive intention to use one. The goal of this thesis is to investigate this mismatch and to propose and test the use of Perceived Learning Self-regulation and Perceived Cognitive Absorption as predictors of the intention to use an LMS and to design and test interventions that improve the Continued Intention to Use an LMS that enhances Perceived Learning Self-Regulation and Perceived Cognitive Absorption. Three intervention tools were designed on a theoretical basis and then implemented: herd behaviour was the basis for Tracking Technology, goal setting was the basis for Visualised Competency, and social learning theory was the basis for Social Media. The intervention designs were based on data from interviews, focus group discussions and online collaboration with 10 teachers. They were implemented on a computer science module with 400 registered students. Two questionnaires were circulated to examine the effects of these interventions on the PLSR, PCA and CIU (151 students) and assess their opinions (149 students). All three interventions increased students' perceived cognitive absorption and perceived learning self-regulation and increased their continued intention to use a learning management system. Moreover, perceived cognitive absorption was found to be a critical antecedent to perceived learning self-regulation, which plays a mediating role between perceived cognitive absorption and their continued intention to use a learning management system. The survey analysis reported a positive perception overall among the students of the proposed interventions and the LMS with the given technology. Interaction analysis showed the continuous and consistent use of the intervention by the learners. The main contribution to knowledge here is a new framework for interventions that can improve students perceived cognitive absorption and thereby their continued intention to use an LMS. This research integrated the theories of experience flow, self-regulation, herd behaviour and goal setting to explain the potential effects of tracking technology, visualised competency, and social media on the perceived learning self-regulation and perceived cognitive absorption, which improved the continued intention to use a learning management system. According to the Information System Success Model, positive attitudes and the perception of benefits can be significant predictors of the intention to use a certain technology. Thus, Perceived Learning Self-Regulation and Perceived Cognitive Absorption were used to propose predictors of students’ continued intention to use a learning management system, instead of their perception of and attitude to possible benefits. For this reason, the present research aimed to develop a framework that introduced, evaluated, and examined the impact of interventions on improving learners perceived cognitive absorption and perceived learning self-regulation as well as affecting learners’ continued intention to use in LMS. To fulfil this aim, the main research question was, “How to improve students’ Continued Intention to Use (CIU) an LMS by improving their perceived learning self-regulation and perceived cognitive absorption?” The results suggest that all interventions had a significant effect on the perceived cognitive absorption, perceived learning self-regulation and continue intention to use the LMS. perceived cognitive absorption was found to be a critical antecedent to the perceived learning self-regulation, which plays the mediating role between perceived cognitive absorption and continue intention to use LMS. The survey analysis also reported overall positive perceptions among students of the use of these interventions and the LMS with the technology. By using interaction analysis, the intervention showed continuous and consistent use among learners. The main contribution to knowledge, as noted above, is a new framework to propose interventions that can improve the perceived cognitive absorption, and in turn, the continue intention to use can be improved. This research integrated experience flow, self-regulation, herd behaviour and goal-setting theories to explain the potential effects of the tracking tool, visualised competency, and social media on the perceived learning self-regulation and perceived cognitive absorption, which improved the learners continue intention to use learning management system

The effect of the tracking technology on students’ perceptions of their continuing intention to use a learning management system

This research examines the effect of having a tracking technology in a learning management system (LMS) that reports the effect of perceiving other students’ interactions on a learner’s intention to keep using LMS in the future. The main underlying theory is herd behaviour theory which argues that crowd behaviour affects the perceptions of the observers. In this paper, we proposed and found that tracking technology will affect a learner’s perceptions of cognitive absorption and that perception of self-regulation from using an LMS. These perceptions are found to influence the learner’s intention to keep using the LMS in the future positively. This research developed a new tracking technology in response to weaknesses noted in the literature and validated by interviewing teachers. Its effects were tested on 151 university students taking a computer science module. This research contributes to knowledge by integrating herd behaviour theory into the design of LMS and offers a new perspective on learners’ interactions with educational technologies

The Impact of Cultural Familiarity on Students’ Social Media Usage in Higher Education

Using social media (SM) in Higher education (HE) becomes unavoidable in the new teaching and learning pedagogy. The current generation of students creates their groups on SM for collaboration. However, SM can be a primary source of learning distraction due to its nature, which does not support structured learning. Hence, derived from the literature, this study proposes three learning customised system features, to be implemented on SM when used in Higher Education HE. Nevertheless, some psychological factors appear to have a stronger impact on students’ adoption of SM in learning than the proposed features. A Quantitative survey was conducted at a university in Uzbekistan to collect 52 undergraduate students’ perception of proposed SM learning customised features in Moodle. These features aim to provide localised, personalised, and privacy control self-management environment for collaboration in Moodle. These features could be significant in predicting students’ engagement with SM in HE. The data analysis showed a majority of positive feedback towards the proposed learning customised SM. However, the surveyed students’ engagement with these features was observed as minimal. The course leader initiated a semi-structured interview to investigate the reason. Although the students confirmed their acceptance of the learning customised features, their preferences to alternate SM, which is Telegram overridden their usage of the proposed learning customized SM, which is Twitter. The students avoided the Moodle integrated Twitter (which provided highly accepted features) and chose to use the Telegram as an external collaboration platform driven by their familiarity and social preferences with the Telegram since it is the popular SM in Uzbekistan. This study is part of an ongoing PhD research which involves deeper frame of learners’ cognitive usage of the learning management system. However, this paper exclusively discusses the cultural familiarity impact of student’s adoption of SM in HE

Real-time object subspace searching based on discrete searching paths and local energy

In automatic visual inspection, the object image subspace should be segmented and matched quickly so that the affine relationship can be built between the template image and the sample image. When the interference is strong and the illumination is uneven, for example in an industrial application, this can make it difficult to obtain an objects subspace quickly and accurately in real-time. In this paper, a novel strategy is proposed to adopt discrete radial search paths instead of searching all points in an image. Therefore, the searching time can be substantially reduced. In order to reduce the influence coming from the industrial environment, the paper proposes another method that is local energy level set segmentation, which can locate the object subspace more efficiently and accurately. The detection of “crown caps” is presented as an example in this paper. Detection effects and computing time are compared between several detection methods, and the mechanisms of inspection have also been analyzed