An inquiry into the effectiveness of student generated MCQs as a method of assessment to improve teaching and learning

 In anticipation of helping students mature from passive to more active learners while engaging with the issues and concepts surrounding computer security, a student generated Multiple Choice Question (MCQ) learning strategy was designed and deployed as a replacement for an assessment task that was previously based on students providing solutions to a series of short-answer questions. To determine whether there was any educational value in students generating their own MCQs students were required to design MCQs. Prior to undertaking this assessment activity each participant completed a pre-test which consisted of 45 MCQs based on the topics of the assessment. Following the assessment activity the participants completed a post-test which consisted of the same MCQs as the pre-test. The pre and post test results as well as the post test and assessment activity results were tested for statistical significance. The results indicated that having students generate their own MCQs as a method of assessment did not have a negative effect on the learning experience. By providing a framework to the students based on the literature to support their engagement with the learning material, we believe the creation of well-structured MCQs resulted in a more advanced understanding of the relationships between the concepts of the learning material as compared with plainly answering a series of short-answer questions from a textbook. Further study is required to determine to what degree this learning strategy encouraged a deeper approach to learning.

Transfer learning using the online Fuzzy Min-Max neural network

In this paper, we present an empirical analysis on transfer learning using the Fuzzy Min–Max (FMM) neural network with an online learning strategy. Three transfer learning benchmark data sets, i.e., 20 Newsgroups, WiFi Time, and Botswana, are used for evaluation. In addition, the data samples are corrupted with white Gaussian noise up to 50 %, in order to assess the robustness of the online FMM network in handling noisy transfer learning tasks. The results are analyzed and compared with those from other methods. The outcomes indicate that the online FMM network is effective for undertaking transfer learning tasks in noisy environments.

Transfer learning using the online FMM model

In this paper, we present an analysis on transfer learning using the Fuzzy Min-Max (FMM) neural network with an online learning strategy. Transfer learning leverages information from the source domain in solving problems in the target domain. Using the online FMM model, the data samples are trained one at a time. In order to evaluate the online FMM model, a transfer learning data set, based on data samples collected from real landmines, is used. The experimental results of FMM are analyzed and compared with those from other methods in the literature. The outcomes indicate that the online FMM model is effective for undertaking transfer learning tasks.

An online transfer learning RBF neural network for cross domain data classification

In this paper, a Radial Basis Function Network (RBFN) trained with the Dynamic Decay Adjustment (DDA) algorithm (i.e., RBFNDDA) is deployed as an incremental learning model for tackling transfer learning problems. An online learning strategy is exploited to allow the RBFNDDA model to transfer knowledge from one domain and applied to classification tasks in a different yet related domain. An experimental study is carried out to evaluate the effectiveness of the online RBFNDDA model using a benchmark data set obtained from a public domain. The results are analyzed and compared with those from other methods. The outcomes positively reveal the potentials of the online RBFNDDA model in handling transfer learning tasks. © 2014 The authors and IOS Press. All rights reserved.

Talking up learning at work : cautionary tales in co‐opting everyday learning

Learning in workplaces is always mediated through talk. It is tempting for management to seek to utilise everyday talk as part of learning and therefore enhance productivity. This paper examines the responses of workers to interventions that aim to formalise informal conversations at work as part of an explicit workplace learning strategy. It draws on interviews with managers and workers in a public sector organisation to examine their experience of these practices. The paper raises questions about whether interventions in the name of fostering informal learning may well be hindering what they seek to promote.

Personality types and languages learning strategies: chameleons changing colours

This exploration of associations between the reported Language Learning Strategy (LLS) preferences of learners of English as a Second Language (ESL) and their personality types is positioned within the contention that the two are generally related. Our findings unequivocally support the existence of this relationship. Moreover, they also provide a platform from which to understand the contribution to learning a second language of two very commonly cited personality traits, introversion/extroversion and neuroticism. However, they also provide the basis for the important caution that the association between personality types and LLS is quite volatile. We have found that it is variation rather than unwavering stability that features in how personality traits apply as predictive of ESL learners' specific LLS preferences. Such prediction is specified even further by the particular contexts of ESL learning where the LLS are applied, for example for listening or speaking and whether this occurs inside or outside a classroom. The implications of these findings for ESL teaching and learning are discussed as is the explanatory power of the chameleon metaphor.

Collaborative learning experience of students in distance education

Educational institutions recognised that the distance education mode is a preferred way to combine study with life, family and work commitments for distance learners. Distance education has played an important role in the provision of educational equity for distance learners who live in remote Australian communities. Engaging students and academic staff will always enhance student-learning outcomes to ensure a positive experience in distance education. It can be effectively achieved through collaborative learning. In distance education, academic staff and students face a number of challenges such as lack of student motivation, high student attrition rates, and a sense of isolation from a university community. Collaborative learning experience will enhance learner-staff and learner-learner interactions in distance learning, which can be achieved through developing a learning process. The learning process for distance learners involves student-learning strategy, Staff interactive sessions, peer-to-peer support, e-assessment, and self-realization of graduate learning outcomes. This distance learning process is confined for Deakin University learning environment, however the expectations is that the distance learning will be more mainstream in future of learning and teaching in Australian institutions. The focus of this research is to analyse and share collaborative learning experience of distance learners (off-campus) students in project management unit. It helps to analyse the barriers in distance education and finding ways to initiate collaborative programs in future. It also helps to fulfil the distance learners’ expectations on program delivery.

The teaching of cost planning using the building cost information service on-line system

This paper describes teaching and learning in building design cost management for undergraduate construction economics, construction management and architecture students using the WorldWideWeb based Building Cost Information Service. The paper also discusses the benefits of using such an authentic learning strategy to achieve specified learning outcomes which are considered by t he a uthors to be of considerable benefit in the teaching of this important subject area. Although there are a number of limitations in the application of building design cost management, as considered in this paper, some of which are discussed below, to date no satisfactory alternative has been developed, and therefore, a clear understanding of what the process is and how it is applied, is essential.

A case study on classification reliability

The reliability of an induced classifier can be affected by several factors including the data oriented factors and the algorithm oriented factors. In some cases, the reliability could also be affected by knowledge oriented factors. In this paper, we analyze three special cases to examine the reliability of the discovered knowledge. Our case study results show that (1) in the cases of mining from low quality data, rough classification approach is more reliable than exact approach which in general tolerate to low quality data; (2) Without sufficient large size of the data, the reliability of the discovered knowledge will be decreased accordingly; (3) The reliability of point learning approach could easily be misled by noisy data. It will in most cases generate an unreliable interval and thus affect the reliability of the discovered knowledge. It is also reveals that the inexact field is a good learning strategy that could model the potentials and to improve the discovery reliability.

The impact of sample size and data quality to classification reliability

The reliability of an induced classifier can be affected by several factors including the data oriented factors and the algorithm oriented factors [3]. In some cases, the reliability could also be affected by knowledge oriented factors. In this chapter, we analyze three special cases to examine the reliability of the discovered knowledge. Our case study results show that (1) in the cases of mining from low quality data, rough classification approach is more reliable than exact approach which in general tolerate to low quality data; (2) Without sufficient large size of the data, the reliability of the discovered knowledge will be decreased accordingly; (3) The reliability of point learning approach could easily be misled by noisy data. It will in most cases generate an unreliable interval and thus affect the reliability of the discovered knowledge. It is also reveals that the inexact field is a good learning strategy that could model the potentials and to improve the discovery reliability.

Goal-efficacy framework : an examination of domestic and international accounting students' academic performance

This study considers the psychological influences on academic performance using a goal-efficacy framework. Data were gathered using a survey questionnaire (N = 375). The paper is motivated by a repeated high failure rate for a second-year core accounting unit and anecdotal evidence that international students perform poorly in comparison with domestic students. The results demonstrate the role of self-regulated learning strategy as a mediating variable for goal orientation and academic performance. While the analyses suggest no significant differences between domestic and international students with respect to the main psychological variables and academic performance, further analyses reveal that four specific factors of the main psychological variables are significantly different between domestic and international students. © 2013 AFAANZ.

Modified AHP for gene selection and cancer classification using type-2 fuzzy logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.