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 purpose is to assist researchers in 1) conducting research surveys, 2) efficiently and effectively retrieving information (such as important researchers, research groups, keywords), and also 3) providing analytical information relating to past and current research trends in a particular field. Their CTSE system employs data-processing technologies and emphasizes the use of a “Learn by Searching” learning strategy to support students to analyze such research trends. To show the effectiveness of CTSE, a pilot experiment has been conducted, where participants were assigned to do research survey tasks and then answer a questionnaire regarding the effectiveness and usability of the system. The results showed that the system has been helpful to students in conducting research surveys, and the research trend transitions that our system presented were effective for producing research trend surveys. Moreover, the results showed that most students had favorable attitudes toward the usage and usability of the system, and those students were satisfied in gaining more know ledge in a particular research field in a short period.

Due to the large number of learning objects (LOs) freely available in the public web-based domain, and the desire of many learners to access such learning materials or LOs on their mobile phones/devices, there is a strong motivation to be able to convert web-based LOs into mobile LOs for reuse on different mobile technologies. We are very interested in the teaching and learning of computing and, more specifically, the Java programming language. Therefore, we conducted an online investigation where we searched and collected information relating to those university computing departments in the English-speaking world offering computing LOs for educational purposes, in order to gather all Java LOs written in the English-language from the public domain. A large number and diverse set of Java LOs was revealed which can be reused for effective educational purposes in web-based environments. Additionally, we examined how web-based LOs can be deployed on different mobile technologies for effective mobile learning.

Prevention and detection of plagiarism have formed the basis for many research projects, but student perceptions on plagiarism are arguably not well understood, and this is particularly true in the computing disciplines. This paper considers two aspects of the student experience, (i) the types of plagiaristic activity that students engage in, and (ii) the specific understanding of what plagiarism means for students who write computer programs. In a recent study we classified types of plagiarism in computing, collecting data from two sources: published material (books, published papers, web sites), and on-line formative quizzes and questionnaires used by universities to test student knowledge of what constitutes plagiarism. Facet analysis was used to categorise the data into four initial categories (sources, actions, material, extrinsic). Further analysis suggested a refinement to six categories and 23 sub-categories which directly relate to the computing disciplines. In a further study we analysed data collected from 18 UK institutions via an online questionnaire, in which over 700 computing students were invited to scrutinise fifteen scenarios which may (or may not) contain plagiarism, and to identify which they thought did contain plagiarism. The results of this study suggest that there are certain types of plagiaristic activity which are poorly understood, and demographic factors which correlate with student understanding. This paper summarises and compares the results of these two studies and reflects on the implications for educating computing students about how they should avoid plagiarism.

In recent years, mobile and context-aware technologies have been used in an attempt to enhance students‟ formal and informal learning experiences both in the classroom and outdoor settings. Such technologies have supported teaching and learning in a variety of subjects. In this paper, we present the ongoing research efforts in our Geometry Mobile (GeM) project, where we conducted a number of trials in the last two years with primary and secondary school students in Sweden. The current trial of our GeM project (April 2011) is discussed together with the data collection method we have used for exploring how Context-Aware learning hints (CA hints) can be utilized to support the different stages of the learning activity. Insights and preliminary results from this trial are described. We also present the motivation of our proposed CA hints, the definition of the requirements of those, and the formulation of a classification thereof.