Jane Yin-Kim Yau

A Practice-Oriented Review of Learning Objects

Reusable learning objects support packaging of educational materials allowing their discovery and reuse. Open educational resources emphasize the need for open licensing and promote sharing and community involvement. For both teachers and learners, finding appropriate tried and tested resources on a topic of interest and being able to incorporate them within or alongside other learning materials can enrich provision and share best practice. Resources are made available by a number of general and subject-specific repositories, but there are also many educational resources residing outside these repositories which may provide useful additional materials. Potential users of materials need to be able to locate relevant material and to assess it with respect to a number of factors (such as suitability for purpose and license requirements). However, even such basic requirements can be less than straightforward to determine. This paper presents a view of the field from the users perspective, bringing together themes from existing research relating to practice-oriented concerns including discoverability, reusability, and quality. It provides a background in this area, exploring current trends, controversies, and research findings. The discussion is also aligned with current provision and practice, indicating areas where further research, provision, and support would be useful.

A mobile context-aware framework for managing learning schedules – data analysis from a diary study

Mobile learning applications can be categorized into four generations: non-adaptive, learning-preferences based adaptive, learning-contexts-based adaptive and learning-contexts-aware adaptive. The research on our learning schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using them as the basis for selecting appropriate learning materials. We have adopted a pedagogical approach for evaluating this framework, an exploratory interview study with potential users consisting of 37 university students. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Our data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario and a proposed taxonomy for describing mobile learner preferences.

Architecture of a Context-aware and Adaptive Learning Schedule for Learning Java

Novice programmers require large amounts of time and motivation to learn an object-oriented programming language such as Java. In this paper, the architecture of our context-aware and adaptive learning schedule (CALS) tool is described. The tool has been designed to focus initially on supporting first year computer science undergraduate students to become more proficient Java programmers, and makes use of a learning schedule, where the learner inputs their daily activities. Based on this information, the tool is able to automatically determine the contextual features such as the location and available time. The appropriate learning materials are selected for the students according to, firstly, the learner preferences (such as learning styles), and secondly the contextual features (such as the level of concentration).

A Context-Aware Personalized M-learning Application Based on M-learning Preferences

The purpose of this paper is to present the data analysis obtained from our interview study, which showed that participants had different individual mobile learning (hereafter, abbreviated as m-learning) preferences. The understanding of these preferences for different m-learning requirements can be used as a foundation for building successful personalized m-learning applications catered to learners’ individual m-learning needs. Participants’ dynamic m-learning preferences (including location of study, noise/distractions level in a location, and time of day) are described. We propose a context-aware personalized m-learning application based on these m-learning preferences. Six scenarios are given to illustrate the m-learning preferences of different learners. The system architecture consists of a learner profile, personalization mechanism and learning objects repository. An initial m-learning preferences questionnaire is used to obtain learners’ dynamic m-learning preferences. Current context values are retrieved from context-aware technologies. Appropriate learning objects are selected to learners based on their preferences and context values.

Source-code plagiarism in universities: a comparative study of student perspectives in China and the UK

There has been much research and discussion relating to variations in plagiaristic activity observed in students from different demographic backgrounds. Differences in behaviour have been noted in many studies, although the underlying reasons are still a matter of debate. Existing work focuses mainly on textual plagiarism, and most often derives results by studying (small) groups of overseas students studying in a Western context. This study investigates understanding of source-code plagiarism (i.e. plagiarism of computer programmes) amongst university students in China. The survey instrument was a Chinese translation of a survey previously administered in English in the UK. This paper reports the results of the exploratory survey conducted in China, and compares these results to those from a parallel survey conducted in the UK. The results show that there is a significant difference in understanding between the respondents from the two surveys, and suggest topics which a future and more comprehensive study may focus on.

An adaptive context-aware mobile learning framework based on the usability perspective

Three different types of context-aware mobile learning applications were revealed in our literature review – location-dependent, location-independent and situated learning. Our theoretical framework – mobile context-aware adaptive learning schedule was constructed. This uses a proactive approach – the learners schedule (i.e. an electronic organiser) to retrieve the location and time available contexts. Other contexts are also considered – learning styles, knowledge level, concentration level and frequency of interruption. Thereafter, appropriate learning materials are recommended to students based on this information. We utilised a diary: diary-questionnaire research methodology for our usability feasibility in which 32 university students had participated in.

Research trends with cross tabulation search engine

To help researchers in building a knowledge foundation of their research fields which could be a time-consuming process, the authors have developed a Cross Tabulation Search Engine (CTSE). Its purp ...

Technical feasibility of a mobile context-aware (social) learning schedule framework

The purpose of this paper is to show the technical feasibility of implementing their mobile context-aware learning schedule (mCALS) framework as a software application on a mobile device using current technologies, prior to its actual implementation. This process draws a set of compatible mobile and context-aware technologies at present and can be used as a reference point for implementing generic mobile context-aware applications. The authors’ mCALS framework retrieves the learner’s location and available time contexts via the built-in learning schedule (i.e., electronic organizer) on a mobile device. These contexts together with the learner’s learning styles and knowledge level (on a selected topic) are used as the basis for the software application to suggest learning materials that are appropriate for the learner, at the time of usage. This retrieval approach eliminates the use of context-aware technologies and the need to directly request the user to enter context information at the time of usage. The authors develop a fully functional prototype of this framework for learners to plan their individual as well as social learning activities amongst one another to make their individual learning processes collaborative and as a way to enhance individual and social learning experiences.

An SNS-based model for finding collaborative partners

This paper proposes a model, Recommendation of Appropriate Partners (RAP), used on a Social Networking Service (SNS) for locating appropriate “helpers” for users based on individual users’ Chain of Friends (CoF) relationships. Using the RAP model, individual users can participate in a collaborative online community in remote locations, whereby helpers are willing to help other users solve their tasks/problems, and it is intended that both the users and helpers gain knowledge from these interactive online sessions. An example of the RAP-based system was implemented to invite Program Committee members to an international conference. The system was evaluated and the experimental results show that our model is very effective for discovering collaboration partners and finding users with similar interests in order to create communities for providing future and longer-term helping exchange.