Sport-specific factors predicting player retention in junior cricket

Understanding factors that motivate young athletes to continue participation in sport can help key stakeholders cultivate an environment that fosters long-term participation. This investigation sought to determine the performance and participation factors that influenced continued participation in junior cricket. Administration-level data were collected each annual season across a seven-year period by a community-level junior cricket association in Australia and analysed to identify the performance and participation-based predictors of player retention. All players were males aged <16 years. Players were categorised according to whether they remained in (or departed from) the association at the end of each playing season. A multivariate logistic regression model with a stepwise variable selection was employed to identify significant independent predictors of player retention. The number of innings batted and overs bowled were significant participation-related contributors to junior cricket player retention. Performance factors such as the number of wickets taken and the number of runs scored also significantly influenced player retention. Finally, team age group, the number of previous seasons played and age were also significant factors in player retention. This demonstrates that sufficient opportunity for children to participate in the game and expression of skills competence are key factors for retention in cricket.     

Competitive State Anxiety Inventory-2 (CSAI-2): evaluating the Swedish version by confirmatory factor analyses

The Competitive State Anxiety Inventory-2 (CSAI-2) is one of the most frequently used instruments when assessing competitive state anxiety in sport psychology research. However, doubts have been expressed about the factorial validity of both the English and the Greek versions of the scale. Hence, a revised version of the inventory (CSAI-2R) has recently been suggested to be more psychometrically sound (Cox et al., 2003). In the present study, the aim was to evaluate the psychometric properties of the Swedish version of the CSAI-2 using confirmatory factor analyses. A total of 969 athletes (571 men and 398 women) competing in 26 different sports completed the Swedish version of the CSAI-2. Three different factor structures were evaluated: the original three-factor model (with cognitive anxiety, somatic anxiety and self-confidence), a two-factor model in which self-confidence was excluded, and a three-factor model containing 17 items (CSAI-2R). The results revealed that only the 17-item model displayed an acceptable fit to the data. Although some doubts remain about the amount of variance that can be attributed to error variance in the subscales, the results suggest that it is better to use the CSAI-2R rather than the original CSAI-2.     

What makes a landmark effective? Sex differences in a navigation task

In Experiment 1, two groups of female rats were trained in a triangular pool to find a hidden platform whose location was defined in terms of a single a landmark, a cylinder outside the pool. For one group, the landmark had only a single pattern (i.e., it looked the same when approached from any direction), while for the other, the landmark contained four different patterns (i.e., it looked different when approached from different directions). The first group learned to swim to the platform more rapidly than the second. Experiment 2 confirmed this difference when female rats were trained in a circular pool but found that male rats learned equally rapidly (and as rapidly as females trained with the single-pattern landmark) with both landmarks. This second finding was confirmed in Experiment 3. Finally, in Experiment 4a and 4b, male and female rats were trained either with the same, single-pattern landmark on all trials or with a different landmark each day. Males learned equally rapidly (and as rapidly as females trained with the unchanged landmark) whether the landmark changed or not. We conclude that male and female rats learn rather different things about the landmark that signals the location of the platform.     

The Unintentional Effects on Body Donation Programs of a Competency-Based Curriculum in Postgraduate Medical Education

As medical programs place increasing importance on competency-based training and surgical simulations for residents, anatomy laboratories, and body donation programs find themselves in a position of adapting to changing demands. To better assess the demand for “life-like” cadaveric specimens and evaluate the possible impacts that competency-based medical education could have upon the body donation program of McGill University, Canada, the authors tracked, over the course of the last 10 years, the number of soft-embalmed specimens, along with the number of teaching sessions and the residents enrolled in competency-based programs that are using cadaveric material. The results reveal that the number of soft-embalmed specimens used within residency training increased from 5 in 2009 to 35 in 2019, representing an increase from 6% of bodies to 36.5% of the total number of body donors embalmed in this institution. Correspondingly, the number of annual teaching sessions for residents increased from 19 in 2012 to 116 in 2019. These increases in teaching are correlated with increasing number of residents enrolled in competency-based programs over the last 3 years (Pearson r ranging from 0.9705 to 0.9903, and R² ranging from 0.9418 to 0.9808). Those results suggest that the new skill-centered curricula which require residents to perform specific tasks within realistic settings, exhibit a growing demand for “life-like” cadaveric specimens. Institutions’ body donation programs must, therefore, adapt to those greater need for cadaveric specimens, which presents many challenges, ranging from the logistical to the ethical.     

Diversity in the Information Seeking Behaviour of the Virtual Scholar: Institutional Comparisons

The logs of four universities using the OhioLINK journal system were evaluated for a period of fifteen months using deep log analysis methods in order to compare and contrast the information seeking behaviour of their users. Large differences were found, especially between the research and teaching universities. Methodological problems associated with making the comparisons are discussed in some detail especially in terms of defining online sessions.     

Introduction to the special issue on teaching and learning in information retrieval

We present an overview of the special issue in this paper. The main objective is to provide information for lecturers on how to improve the student experience, using current knowledge in the field. To this end we present an overview of six papers covering areas as diverse as tools and methods used to support teaching and learning, pedagogical challenges in teaching mathematics for search, etc.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.