Electronic learning can facilitate student performance in undergraduate surgical education: a prospective observational study

BACKGROUND: Our institution recently introduced a novel internet accessible computer aided learning (iCAL) programme to complement existing surgical undergraduate teaching methods. On graduation of the first full cycle of undergraduate students to whom this resource was available we assessed the utility of this new teaching facility. METHOD: The computer programme prospectively records usage of the system on an individual user basis. We evaluated the utilisation of the web-based programme and its impact on class ranking changes from an entry-test evaluation to an exit examination in surgery. RESULTS: 74.4% of students were able to access iCAL from off-campus internet access. The majority of iCAL usage (64.6%) took place during working hours (08:00–18:00) with little usage on the weekend (21.1%). Working hours usage was positively associated with improvement in class rank (P = 0.025, n = 148) but out-of hours usage was not (P = 0.306). Usage during weekdays was associated with improved rank (P = 0.04), whereas weekend usage was not (P = 0.504). There were no significant differences in usage between genders (P = 0.3). Usage of the iCAL system was positively correlated with improvement in class rank from the entry to the exit examination (P = 0.046). Students with lower ranks on entry examination, were found to use the computer system more frequently (P = 0.01). CONCLUSION: Electronic learning complements traditional teaching methods in undergraduate surgical teaching. Its is more frequently used by students achieving lower class ranking with traditional teaching methods, and this usage is associated with improvements in class ranking

Contextualised Browsing in a Digital Library's Living Lab

Contextualisation has proven to be effective in tailoring \linebreak search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201

Proposing a weighting function for adjusting the Global Information Technology Report Networked Readiness Index Framework

The Global Information Technology Report (GITR) has since 2002 been publishing the Networked Readiness Index (NRI) ratings for a number of countries under the auspices of the World Economic Forum. A number of authors have suggested that the credibility of the NRI is called into question by the non-transparent manner in which the authors report the sources of the data and the methodology that was followed to collect the raw data. Furthermore, that it is clear that there is no fixed formula for the economic policy that suit every individual country, but various widespread procedures normally share some common characteristics. This paper offers a weighting function for adjusting the current NRI final computation based on existing framework. The author claims that computing of the NRI rankings based on this new improved weighting function will minimize the so called ‘digital divide’ alluded to in this paper. It is argued that computing of the NRI rankings based on the author’s proposed weighting function would be more acceptable to the NRI community, by adjusting the current computed final NRI ratings for the benefit of all the economies deemed capable of being members of the GITR NRI community

Solving the Cold-Start Problem in Recommender Systems with Social Tags

In this paper, based on the user-tag-object tripartite graphs, we propose a recommendation algorithm, which considers social tags as an important role for information retrieval. Besides its low cost of computational time, the experiment results of two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity. Especially, it can obtain more personalized recommendation results when users have diverse topics of tags. In addition, the numerical results on the dependence of algorithmic accuracy indicates that the proposed algorithm is particularly effective for small degree objects, which reminds us of the well-known \emph{cold-start} problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree distributions

Combining and selecting characteristics of information use

In this paper we report on a series of experiments designed to investigate the combination of term and document weighting functions in Information Retrieval. We describe a series of weighting functions, each of which is based on how information is used within documents and collections, and use these weighting functions in two types of experiments: one based on combination of evidence for ad-hoc retrieval, the other based on selective combination of evidence within a relevance feedback situation. We discuss the difficulties involved in predicting good combinations of evidence for ad-hoc retrieval, and suggest the factors that may lead to the success or failure of combination. We also demonstrate how, in a relevance feedback situation, the relevance assessments can provide a good indication of how evidence should be selected for query term weighting. The use of relevance information to guide the combination process is shown to reduce the variability inherent in combination of evidence