Peer-supported learning groups: a collaborative approach to supporting students learning in engineering and technology

peer-reviewedThis paper describes a non-traditional tutoring programme based on collaborative peer-support learning approach, and reflects on two years of its implementation to specific subjects in engineering and information technology based courses at the University of Limerick in Ireland. The programme, known as the Peer-Supported Learning Groups (PSLG), is an academic enrichment scheme which has been developed by adapting the SI model such that it meets the needs of the students in Ireland and, at the same time, fits into the Irish third-level education system. The paper begins by giving a rationale for the introduction of the PSLG to the targeted subjects and the reasons for choosing the SI model. This is followed by description of the operational structure of the programme highlighting the difficulties encountered at the initial stages and the measures taken to alleviate these difficulties. Quantitative measures for evaluating the effect of the PSLG on student’s performance, as well as analysis of feedback collected from the students and the leaders, are presented and discussed. The paper concludes by outlining issues for improving the current programme and associated further developments.PUBLISHEDpeer-reviewe

Second generation and perceptual wavelet based noise estimation

peer-reviewedThe implementation of three noise estimation algorithms using two different signal decomposition methods: a second-generation wavelet transform and a perceptual wavelet packet transform are described in this paper. The algorithms, which do not require the use of a speech activity detector or signal statistics learning histograms, are: a smoothing-based adaptive technique, a minimum variance tracking-based technique and a quantile-based technique. The paper also proposes a new, robust noise estimation technique, which combines a quantile-based algorithm with smoothing-based algorithm. The performance of the latter technique is then evaluated and compared to those of the above three noise estimation methods under various noise conditions. Reported results demonstrate that all four algorithms are capable of tracking both stationary and non-stationary noise adequately but with varying degree of accuracyPUBLISHEDpeer-reviewe