Prior Experience and Student Satisfaction with E-Tandem Language Learning of Spanish and English

Recent literature in the field of foreign language learning has indicated that classroom learning is not necessarily enough for students to acquire proficiency in a foreign language. Learners who achieve a high level in the target language often combine work in the classroom with activities outside it. At the same time, a number of studies indicate that, when foreign language learners do work with their target language beyond the classroom, it is often to practice the receptive skills of reading and listening as opposed to the productive ones of speaking and writing. For this reason, students at The College of New Jersey in the United States and Universidad de Alcalá in Spain were paired up to work in tandem to practice Spanish and English through a private Facebook© page and Skype calls©. This paper discusses the impressions of 195 participating students on both sides of the Atlantic to the activities as determined through a questionnaire. The overall objective was to determine if prior experience with the two applications and in using the foreign language in conversation has an impact on student satisfaction. The results indicate prior experience with the applications and with the target language correlates with a positive estimation of the activities. Additional variations were also found. Language instructors who wish to set up an e-tandem experience are advised to assist students with less experience so that they can benefit from the activity

Analysis of the main factors affecting the adoption of cloud based interactive mobile learning in the Australian higher education sector

Today, every business depends on Information Technology (IT) for the efficient service delivery and cost-effective application of technological resources. Modern technologies are being adopted to overcome business pressure, streamline existing procedures and service delivery cost-efficiency for maximising profit due to the increase in global competition and shifts in the customer expectations. Cloud computing (CC) is an Internet-centric computing service that utilises and provides IT services to organisations through the provisioning of resources through the Internet using web-centric software and gadgets without the assistance of any private IT architecture within the firm. Cloud based interactive mobile learning platform is the result of such exploration and this practice of learning is improving with the time. New technologies such as smart mobile devices, Cloud computing and wireless connectivity are opening new opportunities of learning for students. Thus, the aim of this paper is to evaluate the main factors affecting the adoption of Cloud based interactive mobile learning for the Australian Higher Education sector. In this research, a survey data collection technique with existing students using Mobile application for learning and also a literature review process were conducted. Research outcome shows that the use of Artificial intelligence and Machine learning can make learning more efficient and these technologies need to be integrated in applications designed for Australian Higher Education sector. It is expected that the research outcome will help interactive mobile application developers and higher education providers to better understand the requirements of students while providing an interactive learning platform for them. © International Association of Online Engineering

Analysis of the Main Factors Affecting the Adoption of Cloud based Interactive Mobile Learning in the Australian Higher Education Sector

Today, every business depends on Information Technology (IT) for the efficient service delivery and cost-effective application of technological resources. Modern technologies are being adopted to overcome business pressure, streamline existing procedures and service delivery cost-efficiency for maximising profit due to the increase in global competition and shifts in the customer expectations.  Cloud computing (CC) is an Internet-centric computing service that utilises and provides IT services to organisations through the provisioning of resources through the Internet using web-centric software and gadgets without the assistance of any private IT architecture within the firm. Cloud based interactive mobile learning platform is the result of such exploration and this practice of learning is improving with the time. New technologies such as smart mobile devices, Cloud computing and wireless connectivity are opening new opportunities of learning for students.  Thus, the aim of this paper is to evaluate the main factors affecting the adoption of Cloud based interactive mobile learning for the Australian Higher Education sector. In this research, a survey data collection technique with existing students using Mobile application for learning and also a literature review process were conducted.  Research outcome shows that the use of Artificial intelligence and Machine learning can make learning more efficient and these technologies need to be integrated in applications designed for Australian Higher Education sector.  It is expected that the research outcome will help interactive mobile application developers and higher education providers to better understand the requirements of students while providing an interactive learning platform for them.   </p

Technology enabled categorisation of learners for improved support in experiential learning.

The purpose of this thesis is to examine which data captured by experiential learning technology can be used to understand more about students’ perspectives, mindsets and skills. The objective is to examine how technology-enabled real-time analysis of learner data can be used by learning facilitators and instructional designers to improve the practice of experiential learning in higher education institutions. The study adopts an anti-positivist perspective that acknowledges habit as a driver of deterministic behaviour and that deterministic behaviour can be examined using scientific methods. The data used in this research is retrospectively de-identified student learning data captured by an experiential learning technology which has been used to structure and support the facilitation of an experiential business project program. The research findings outline the quantitative outcomes followed by an integrative qualitative discussion that explores how the findings could be used to inform the practice of experiential learning design and facilitation. Specifically the methodology outlines: the experiential business project program design, the classification of learning tasks into independent variable categories, and the results of student responses to three surveys. The three surveys being: the Revised Implicit Theories of Intelligence Survey, Revised Two Factor Study Process Questionnaire and a learning history survey and the manner in which these surveys were dummy coded into dependent variables, with a detailed description of how the regression analysis is conducted. The results section presents and examines the five regression models developed. The purpose of the examination is to explore the extent to which learner data from an experientially developed learning technology could be used to understand more about students’ perspectives, mindsets and skills. The integrative discussion examines each of the three research questions explicitly. The discussion focused on research question one examines the nature of the learning tasks that have a significant relationship with one or more of the learning theory based dependent variables. It investigates whether there is an alignment between what is known about the nature of learners who exhibit or employ a particular mindset, approach to learning or learning history and the learning task categories use as independent variables in the five regression models presents in the results. The discussion focused on research question two examines what additional learning data could be captured to improve the predictive power of the five regression models. The discussion focused on research question three examines how displaying predictive insights, using learner data, alongside learning theory insights could be used by instructional designers and learning facilitators. The discussion explores how facilitators and learning designers could use the information to customise facilitator support, aid in the development of incentives that encourage learners to engage with learning content that they do not naturally lean towards and support the adaption of learning content to align better with a learner's motives. This study further proposes an example of the benefits of integrating learning analytics and learning theory, how learning theory based analysis could enable more use of experiential learning within higher education institutions, enable experiential learning facilitators to provide more tailored support of students during experiential learning programs and how the results of the analysis could help students extract more of the benefits from the available learning out of experiential learning programs