Online Reputation Model Using Multiple Quality Factors

Users on the internet are looking for ways to minimize their experiences in performing online transactions. Reputation systems as a decision support tool are trying to facilitate online transactions. However, many reputation systems use Naïve methods to compute the reputation of an item. These methods are unstable when there is sparsity in the ratings. Also, they cannot discover trends emerging from recent ratings. Other methods, which use weighted average or probabilistic model, usually focus on one aspect of the reviewer ratings. Even though models that combine multiple factors often accomplish that through an arbitrary set of weights. This research study looks at various aspects of reviewers’ ratings and proposes a new reputation model that attempts to assess the reviewer reputation by combining four factors through a Fuzzy model. These weights are then involved in computing the item reputation. The proposed reputation model has been validated against state-of-art reputation models and presented significant accuracy regarding Mean Absolute Errors (MAE) and Kendall correlation. The proposed reputation model also works well with the sparse and dense dataset

Challenges and strategies for assessing student workplace performance during work-integrated learning

This study explores the challenges of assessing student workplace performance during work-integrated learning. It highlights the need for, yet difficulties with, combining positivist and constructivist assessments where workplace supervisors make evaluative judgements on performance yet students are also agents in their own assessment. It examines the ratings awarded by 163 workplace supervisors for 213 business undergraduates completing a work placement as part of their degree program in Western Australia. Students were rated on 17 capabilities associated with employability and results indicate, in alignment with previous studies, a tendency among supervisors to assign inflated marks across capabilities. The mean capability rating awarded to each student was significantly higher than their weighted course average, suggesting workplace supervisors mark more highly than academics in coursework units. To identify solutions to manage leniency bias, the study examined variations in supervisor ratings for a range of personal and contextual variables such as gender, organisation size, work area and sector. Although supervisor ratings were inflated, they were consistent across the sample with variations recorded for only four capabilities in certain work areas. Reasons for leniency bias among workplace supervisors are explored in light of the findings and alternative approaches to evaluating student workplace performance are presented

Marker effects and examination reliability: a comparative exploration from the perspectives of generalizability theory, Rasch modelling and multilevel modelling

This study looked at how three different analysis methods could help us to understand rater effects on exam reliability. The techniques we looked at were: generalizability theory (G-theory) item response theory (IRT): in particular the Many-Facets Partial Credit Rasch Model (MFRM) multilevel modelling (MLM) We used data from AS component papers in geography and psychology for 2009, 2010 and 2011 from Edexcel.</p