Measuring performance of virtual learning environment system in higher education

Purpose – The purpose of this paper is to measure the performance of commercial virtual learning environment (VLE) systems, which helps the decision makers to select the appropriate system for their institutions. Design/methodology/approach – This paper develops an integrated multiple criteria decision making approach, which combines the analytic hierarchy process (AHP) and quality function deployment (QFD), to evaluate and select the best system. The evaluating criteria are derived from the requirements of those who use the system. A case study is provided to demonstrate how the integrated approach works. Findings – The major advantage of the integrated approach is that the evaluating criteria are of interest to the stakeholders. This ensures that the selected system will achieve the requirements and satisfy the stakeholders most. Another advantage is that the approach can guarantee the benchmarking to be consistent and reliable. From the case study, it is proved that the performance of a VLE system being used at the university is the best. Therefore, the university should continue to run the system in order to support and facilitate both teaching and learning. Originality/value – It is believed that there is no study that measures the performance of VLE systems, and thus decision makers may have difficulties in system evaluation and selection for their institutions.

The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

Post-observation feedback as an instigator of teacher learning and change

This paper investigates an on-going study into the nature of the discourse teacher educators (TEds) and student-teachers (Ts) use during postobservation feedback, and explores the feedback discourse in terms of the impact it may have on teacher learning and change. Two types of feedback, confirmatory and corrective, are briefly discussed, and the former is then made the focus of the paper in its role as a potential instigator of teacher learning and change.

An assessment of the learning benefits of using a web-based learning environment when teaching accounting

The impact and use of information and communication technology on learning outcomes for accounting students is not well understood. This study investigates the impact of design features of Blackboard 1 used as aWeb-based Learning Environment (WBLE) in teaching undergraduate accounting students. Specifically, this investigation reports on a number of Blackboard design features (e.g. delivery of lecture notes, announcements, online assessment and model answers) used to deliver learning materials regarded as necessary to enhance learning outcomes. Responses from 369 on-campus students provided data to develop a regression model that seeks to explain enhanced participation and mental effort. The final regression shows that student satisfaction with the use of a WBLE is associated with five design features or variables. These include usefulness and availability of lecture notes, online assessment, model answers, and online chat.