The Quest for Equilibrium : Towards an Understanding of Scalability and Sustainability for Mobile Learning

The research presented in this thesis investigates the concept of sustainability in relation to mobile learning initiatives. Sustainability is seen as a key concept for mobile learning to gain acceptance. In linking sustainability to scalability, a term used to describe how well something can grow to suit an increasing complexity, a representation of this process is provided. In this thesis, this process is called ``the quest for equilibrium.'' A study was conducted of an actual mobile learning initiative that involved introducing podcasts as a supplement to traditional lectures in higher education. In following this initiative, thorough data gathering was conducted, utilizing the process of iterative cycles that characterizes the action research approach. In accordance, a literature survey was conducted, whereby leading publications in mobile learning were classified and analyzed according to the following criteria: Reflections, Frameworks, Scalability, and Sustainability. As the mobile learning system evolved from idea to an actual empirical study, trying to understand this process became important. The insights gained during this research were used to develop a conceptual model that is based on the notion that the two concepts of Scalability and Sustainability can be linked to each other. This conceptual model is presented describing how a mobile learning system evolves, from Idea, to Experiment, to Project, to Release. Further, each of the stages in this evolution is described by using four areas of concern: Technology, Learning, Social, and Organization. Using the experience from a specific mobile learning initiative to define a conceptual model that then is used to describe the same initiative, was a way to bring together practice, theory, and research, thus provide reliable evidence for the model itself. The conceptual model can serve as a thinking tool for mobile learning practitioners, to help address the complexity involved when undertaking new efforts and initiatives in this field

Understanding Scalability and Sustainability in Mobile Learning : A Systems Development Framework

The rapid development of mobile technologies combined with access to content almost everywhere and every time allows people to experience new situations regarding learning in a wide variety of situations. Mobile learning brings the promise of learning "on the move" by allowing learners to take control over time and space, thus making learning "more natural". The field of mobile learning has rapidly evolved in the last ten years and many initiatives have been conducted worldwide. However, research results indicate that few of these efforts have produced any lasting outcomes. It is evident that these initiatives are faced with inherently complex settings and that the outcomes might not live up to their prom- ises; will not be adopted and, hence, will not become sustainable. Many of the complex issues faced by mobile learning initiatives are similar to those faced by the development of information systems. This latest statement suggests that an improved development practice might hold one piece of the key to sustainable mobile learning. The aim of the research presented in this thesis is to investigate the relation between information systems development practice and mobile learning development; and if methods and models originated within information systems development can be used to strengthen mobile learning initiatives. In order to investigate this relation, this thesis studies several mobile learning initiatives with a particular focus on how and why development and research was initiated and conducted. Concepts found in mobile learning practices are strengthened by providing a theoretical perspective with roots in information systems development. The outcomes of the studies presented in this thesis indicate that the development practice of mobile learning initiatives can be redefined in order to achieve more sustainable results. The core of this thesis consists of eight peer-reviewed scientific publications that have been presented at different international conferences. Five of the papers explore the field of mobile learning and its practice while the other three publications present the central ideas that serve as the basis for the proposed framework, how it has been developed, and the motivations behind its creation. The main contribution of this thesis is a novel development framework aimed at researchers and practitioners in the field of mobile learning. The framework defines the life-cycle of a mobile learning initiative and identifies the importance of emphasizing the concepts of scalability and sustainability during the development process. This may be a way to reduce the complexity inherent to mobile learning and its settings, and a means to improve the outcomes of coming mobile learning initiatives in terms of long lasting usable results

Was it Pod Worthy?:A Preparatory Plan for Evaluating Podcasting in Higher Education

Students and teaching staff in higher education are constantly looking for new tools to help them study and teach more efficiently. The University of Canterbury began ProjectPodcast to introduce podcasting to a number of subjects as an add-on to the current course curriculum. Podcasting is being used to enhance mobile learning and enthuse both students and lecturers. Previous podcasting evaluations show that there exists a need for both audio content from lectures, or so called “LectureCasts” as well as supplementary material or “Sup!Casts”. In this study, we will be evaluating ProjectPodcast. The evaluation is aimed at both the student population as well as the lecturing staff in order to gain knowledge about their impressions of podcasting. Prior podcasting surveys have received low response rates, due to the choice of time, location and medium. Hence, our evaluation plan has been created with the goal of encouraging feedback from students and lecturers. In this paper we present our preparatory plan for evaluating ProjectPodcast

How Tool Support and Peer Scoring Improved Our Students' Attitudes Toward Peer Reviews

We wanted to introduce peer reviews for the final report in a course on Software Testing. The students had however experienced issues with peer reviews in a previous course which made this a challenge. To get a better understanding of the situation, we distributed a pre-questionnaire to the students. 48 of the 83 students provided their expectations on peer reviews. To deal with some of the perceived issues, we developed a peer review tool where we introduce anonymity, grading of reviews, teacher interventions, as well as let students score and comment on the reviews they receive. In total, 67 reports were submitted by 83 students and 325 reviews were completed. The post-questionnaire was answered by 48 students (not necessarily the same respondents as for the pre-questionnaire as both were collected anonymously). While 27 of the students expected incorrect feedback only 13 students agreed to have got incorrect feedback in the post-questionnaire. The students reported that they found the feedback from their peers more valuable (+15%) than expected, and 88% of the students reported that they learned from doing peer reviews. Overall, we find that the students' attitudes towards peer reviews have improved

Asked and Answered : Communication Patterns of Experts on an Online Forum

The notion of network structure for social relations dates back half a century. Nowadays people form social networks offline as well as online. At an online community people are connected trough information exchange of sorts. Interest groups form often forums to aid each other and discuss things. Programmers are no exception and a question and answer site called Stack Overflow has been up and running since 2008. Our focus is to find patterns of how people interact on this online community and see if we can find expert users. We find 4 different ways to categorize experts, which result in different rankings. We also investigate how expertise is divided among topics, and find some overlap with the global ranking.

Current State and Next Steps on Automated Hints for Students Learning to Code

The core of this work-in-progress is that the best way to learn how to code is to practice by solving problems. However, if students have trouble with this, they can get frustrated and give up. Automated Tutoring Systems (ATS) aim to provide hints to help them solve the problems they encounter. Many of the existing systems offer general hints, e.g., “check the conditional statement” or help the student interpret the compiler or test-case errors. While this can be useful, we think that an ATS should provide interactive and specialized feedback for each program. We snowballed through publications on promising ATS and found that there are several such systems (in 27 publications), but we could also identify many challenges and that our requirements were not met by any existing system. For example, few of them work on general-purpose programming languages, e.g., Java, or scale to realistic problems consisting of multiple methods and classes. From the search, we find ATS based on Automated Program Repair (APR) shows the most promise. However, while program repair has the potential to generate specialized hints to help guide the student to a working state, studies that looked into these have identified further challenges. For example, many APR ATS tools only show the repaired program to the students, who then have to compare and modify their program accordingly. Another issue is that APR generally only modifies a few lines, so if the student solution is far from correct, the repair might fail. This can be solved by partial repair, i.e., the program is repaired so at least one additional test-case passes. While this increases the repair rate, it might make hints more difficult or point the students in a non-obvious or even “wrong” direction. The APR can take several minutes, which also makes it unsuitable for interactive ATS. We take a design science approach to define an ATS based on APR that attempts to address the identified challenges. We give a review of the state-of-the-art for the required components, e.g., APR, how to generate hints from differences between two programs. From this, we suggest a threestep roadmap; 1. identify suitable APR-tools, 2. construct an oversized test-suite, and 3. adopt APR to the tutoring context

A Preliminary Study on the Use of Key-words for Source Code to Architecture Mappings

We implement an automatic mapper that can find the corresponding architectural module for a source code file. The mapperis based on multinomial naive Bayes, and it is trained using custom keywords for each architectural module. For prediction,the mapper uses the path and file name of source code elements. We find that the needed keywords often match the modulenames, but also that ambiguities and discrepancies exist. We evaluate the mapper using nine open-source systems and findthat the mapper can successfully create a mapping with perfect precision, but in most cases, it cannot cover all source codeelements. Other techniques can, however, use the mapping as a foothold and create further mappings

To automatically map source code entities to architectural modules with Naive Bayes

Background: The process of mapping a source code entity onto an architectural module is to a large degree a manual task. Automating this process could increase the use of static architecture conformance checking methods, such as reflexion modeling, in industry. Current techniques rely on user parameterization and a highly cohesive design. A machine learning approach would potentially require fewer parameters and better use of the available information to aid in automatic mapping. Aim: We investigate how a classifier can be trained to map from source code to architecture modules automatically. This classifier is trained with semantic and syntactic dependency information extracted from the source code and from architecture descriptions. The classifier is implemented using multinomial naive Bayes and evaluated. Method: We perform experiments and compare the classifier with three state-of-the-art mapping functions in eight open-source Java systems with known ground-truth-mappings. Results: We find that the classifier outperforms the state-of-the-art in all cases and that it provides a useful baseline for further research in the area of semi-automatic incremental clustering. Conclusions: We conclude that machine learning is a useful approach that performs better and with less need for parameterization compared to other approaches. Future work includes investigating problematic mappings and a more diverse set of subject systems