Deploying and utilizing learning objects on mobile phones
Muyinda, Paul Birevu
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An increasing desire to port learning on mobile phones (m-learning) exists. However, there is limited understanding on how to pedagogically obtain access to and use learning objects on mobile phones. The limited understanding is caused by a dearth in frameworks for underpinning the development of applications for pedagogically obtaining access to and utilising learning objects on mobile phones. Following Design Research methodology, this research has developed a Mobile Learning Objects Deployment and Utilisation Framework (MoLODUF) to address the problem. The framework aims at guiding application developers to instantiate/create pedagogic applications that can enable learners in developing countries obtain access to and use learning objects, delivered over the Internet/other networking technologies, regardless of their proximity to higher education institutions, through the use of mobile phones. To develop the MoLODUF, six research questions were answered. In the first research question, we established the current learner contexts, practices and prospects for the development and growth of m-learning. The results have falsified the exiting belief that internal learners are endowed with more learning resources and better learning contexts than distance learners. Association test runs between mode of study and the majority of context variables returned non-significant p-values. For instance, there was no significant association between mode of study with: the learner location (p=0.532), availability of mobile network connectivity (p=0.062), availability of Internet connectivity (p=0.329), availability of power supply (p=0.199), Internet use (p=0.249), mobile phone use (p=0.122), Blackboard LMS use (p=0.148), CDROM use (p=0.266) and computer applications use (p=0.244). There was also lack of significant association between learner’s location with mobile network (p=0.136) and Internet (p=0.329) connectivity. Furthermore, power supply was not associated with availability of mobile network connectivity (p=0.301). Significant associations were found between mode of study and noise levels of learner’s location (p=0.029), learner’s location and its noise levels (p=0.035), learner’s location and power supply (p=0.000), power supply and Internet connectivity (p=0.000), learners’ digital characteristics and age (p = -0.199) and mode of study and use of e-mail (0.002). As far as current m-learning practices were concerned, there existed limited knowledge about the term ‘m-learning’ though m-learning was inadvertently being practiced through push and asynchronous text (45.1%) and audio (45.5%) learning objects. Prospects for the development and growth of m-learning lay in the increasing permeation and processing power of mobile phones, increasing coverage of telecommunication networks, favourable government policies on telecommunications investment, existence of e-learning infrastructure and distance learning units in some universities, high mobility of lifelong learners, increasing elitism, existence of other mobile applications and the need to support great student numbers from programmes like UPE and USE. In the second research question, we established seven (7) non-mutually exclusive candidate m-learning processes namely: Co-Creation of New Knowledge, Knowledge Sharing, Collaborative & Interactive learning, Reflective Learning, Problem-Based Learning, Academic and Administrative Support and Communication/Information Exchange. These learning processes have learning pedagogies underpinned by the social constructivist, conversational, problem-based, reflective and teaching and learning support learning theories. In the third research question, we determined the learning objects that could be used to service the identified learning processes. It was established that amidst the cost and kind of constraints placed by mobile technologies, text and audio based learning objects were feasible. The research however, emphasized the need to leverage different learning object media types in ways that are appropriate to the prevailing circumstances so as to produce the best learning experience. In the forth research question, we adduced from the findings, twelve (12) factors for obtaining access to and utilising learning objects in m-learning. They include the type of: learning objects needed, learning device to be used, learning interfaces at hand, learning connectivity present, learning processes to be accomplished, learning costs involved, learning resources needed, learning contexts available, learning objects user’s profile, learning ethics to be abided by, education technology policy in place and learning evaluation mechanisms available. Therefore in the fifth research question, we used the aforementioned factors to develop a competence set of major and sub-dimensions of the MoLODUF. Consequently, the MoLODUF is composed of twelve (12) major dimensions, which respectively include: M-Learning Object, M-Learning Device, M-Learning Interface, M-Learning Connectivity, Learning Process, M-Learning Costs, M-Learning Resources, M-Learning Context, M-Learning Objects User, M-Learning Ethics, M-Learning Policy and MLearning Evaluation. Using expert evaluation, the MoLODUF dimensions were found to be highly reliable at a standardized Cronbach’s alpha of 0.9227. Using ANOVA, the dimensions were found to be highly valid at a grand mean of 3.6167 for F = 0.6407 and probability of 0.7841 on a four point nominal likert scale of strongly disagree (1) to strongly agree (4). In the sixth research question, the MoLODUF was compared with similar learning frameworks to adduce the contribution. The MoLODUF extends existing e-learning frameworks with five mobility dimensions, namely: MLearning Cost, Learning Processes, M-Learning Objects, M-Learning Policy and M-Learning Context. It also extends existing m-learning frameworks with five dimensions, namely: M-Learning Costs, Learning Processes, M-Learning Evaluation, M-Learning Policy and M-Learning Ethics dimensions. In a nutshell, the MoLODUF provides a competency set of novel guidelines to be followed when creating/instantiating/developing applications for deploying and utilising learning objects on mobile phones. It can also be used to evaluate existing m-learning environments, products and services. Hence through the MoLODUF, the desire to port learning on mobile devices is made possible.