List of speakers for the ITx 2016 conference
This page contains the speaker list for ITx 2016, ordered by name. Check out the Programme as well.
Lecturer
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Adequate computing resources are essential to the effective teaching of Information Technology. There are several complicating factors when these resources are provided in the context of computer laboratories. These include the reliability of machines, consistency of software environments, and adequacy of hardware and the cost in both financial and human resources. We addressed these problems by progressively phasing out desktop computers in laboratories in favour of issuing laptops to IT students. These laptops were of a consistent specification and had a standard software environment.
Practical problems encountered with this approach included procuring appropriate numbers of laptops in a timely manner, challenges with technical support and monitoring of students during practical tests and exams. Procedural problems included security of the laptops, handling returns and meeting student expectations. Each of these problems was solved and we succeeded in creating an efficient, cost-effective and flexible laptop-based environment. This created an improved teaching environment where student fees could be directed to other areas, where technical staff could focus on other issues, and students have greater flexibility in their work. We can therefore recommend a transition to laptop-based teaching for Information Technology students.
The academic success of international students is crucial for many tertiary institutions. Early predictions of students’ learning outcomes allow for targeted support and therefore improved success rates. In this study, international students’ demographic information, past academic histories, weekly class attendance records, and assessment results in an ongoing course were used to develop models to predict student success and failure in the course on a weekly basis. The prediction models were produced with three decision tree classification algorithms: REPTree, J48 tree, and LMT on the data-mining platform WEKA. Of these, the LMT algorithm has the highest level of accuracy, but the REPTree and J48 models are simpler and easier to interpret. While the accuracies of all three models are above 90%, further research is needed to more accurately predict student failure at early stages.