Author ranking evaluation at scale

We evaluate author impact indicators and ranking algorithms on two publication databases using large test data sets of well-established researchers. The test data consists of (1) ACM fellowship and (2) various life-time achievement awards. We also evaluate different approaches of dividing credit of papers among co-authors and analyse the impact of self-citations. Furthermore, we evaluate different graph normalisation approaches for when PageRank is computed on author citation graphs.We find that PageRank outperforms citation counts in identifying well-established researchers. This holds true when PageRank is computed on author citation graphs but also when PageRank is computed on paper graphs and paper scores are divided among co-authors. In general, the best results are obtained when co-authors receive an equal share of a paper's score, independent of which impact indicator is used to compute paper scores. The results also show that removing author self-citations improves the results of most ranking metrics. Lastly, we find that it is more important to personalise the PageRank algorithm appropriately on the paper level than deciding whether to include or exclude self-citations. However, on the author level, we find that author graph normalisation is more important than personalisation.     

A systematic follow‐up approach for mainstreaming students with behavior and emotional disorders

The Behavior Intervention Centres are part of the Houston Independent School District's (HISD) Special Education Department. Their purpose is to provide educational services to elementary and secondary students, aged 3 through 21, who exhibit either severely aggressive or severely withdrawn behaviours. This article describes a formalized follow‐up service considered by the authors to be a critical component of the BIC program.     

How to evaluate rankings of academic entities using test data

In the field of scientometrics, impact indicators and ranking algorithms are frequently evaluated using unlabelled test data comprising relevant entities (e.g., papers, authors, or institutions) that are considered important. The rationale is that the higher some algorithm ranks these entities, the better its performance. To compute a performance score for an algorithm, an evaluation measure is required to translate the rank distribution of the relevant entities into a single-value performance score. Until recently, it was simply assumed that taking the average rank (of the relevant entities) is an appropriate evaluation measure when comparing ranking algorithms or fine-tuning algorithm parameters.With this paper we propose a framework for evaluating the evaluation measures themselves. Using this framework the following questions can now be answered: (1) which evaluation measure should be chosen for an experiment, and (2) given an evaluation measure and corresponding performance scores for the algorithms under investigation, how significant are the observed performance differences?Using two publication databases and four test data sets we demonstrate the functionality of the framework and analyse the stability and discriminative power of the most common information retrieval evaluation measures. We find that there is no clear winner and that the performance of the evaluation measures is highly dependent on the underlying data. Our results show that the average rank is indeed an adequate and stable measure. However, we also show that relatively large performance differences are required to confidently determine if one ranking algorithm is significantly superior to another. Lastly, we list alternative measures that also yield stable results and highlight measures that should not be used in this context.     

构建高等教育教学标准:教师专业发展中心在四所世界一流大学的实践与应用

教师专业发展项目一直强调提升高等教育整体质量,对发展高校科研、教学以及学生的学习都有着举足轻重的作用。本文选取澳大利亚、英国和美国的四所世界一流大学的教师专业发展中心进行研究,阐释了教师专业发展在四所大学的长期实践及其对教师教学和科研水平提高的积极意义,由此总结并提出一系列提高高等教育整体质量的实践经验和关于未来研究方向的建议。     

Determination of muscle activity during running at reduced body weight

The aim of this study was to investigate how lower extremity muscles are influenced by body weight support during running at different speeds. Nine participants (age 24 ± 2 years, height 1.75 ± 0.12 m, mass 73.5 ± 15.7 kg) ran at 100%, 115%, and 125% of preferred speed at 100%, 90%, 80%, 70%, and 60% of body weight on a treadmill that provided body weight support. Preferred speed was self-selected by each participant and represented a speed that he or she could sustain if going for a 30 min run. Electromyography (EMG) data were recorded (1000 Hz, 1 min) from the bicep femoris, rectus femoris, tibialis anterior, and gastrocnemius for each condition together with knee angle (electrogoniometer). Average and root mean square EMG were calculated across 30 s. Muscle patterns were determined by smoothing (low-pass filter, 4 Hz) and extracting patterns for 49 cycles defined by consecutive maximum knee flexion angles. Repeated-measures analyses of variance were used to compare average and root mean square across body weight and speeds. Correlations were computed between the 100% speed/100% body weight condition and all other conditions per muscle. There was no interaction between body weight and speed (P > 0.05). Average and root mean square decreased as body weight decreased for all muscles (P < 0.05) and increased across speeds for all muscles (P < 0.05). Correlations for all muscles between conditions were high (range: 0.921-0.999). Although a percent reduction in body weight did not lead to the same reduction in muscle activity, it was clear that reducing body weight leads to a reduction in muscle activity with no changes in muscle activity patterns.     

Social support,academic adversity and academic buoyancy: a person-centred analysis and implications for academic outcomes

The present study employed person-centred analyses that enabled identification of groups of students separated on the basis of their perceptions of social support (home and community), academic support, academic adversity and academic buoyancy. Among a sample of 249 young people, including many from high-needs communities, cluster analysis revealed three distinct groups of students: the thriver, supported struggler and at-risk struggler. We compared the three groups on their academic motivation. Analyses revealed significant differences between groups in adaptive motivation outcomes, but no differences in impeding or maladaptive motivation outcomes. Combined, the results speak to the importance of support and academic buoyancy for positive student outcomes.     

Multiple Service Use: The impact of consistency in service quality for vulnerable youth

Little is known about the way in which variations in service quality influence outcomes when youth are clients of more than one service system. This article reports on a study of 1,210 adolescents (aged 13–17 years), half were concurrent clients of two or more services and half were not involved in two or more services. Youth completed a self-report questionnaire administered by a trained interviewer. It was hypothesized that youth reporting two positive service experiences would report lower risks, higher resilience, and better outcomes than youth reporting inconsistent or two negative service experiences and that their resilience, risks, and outcomes would be similar to those of youth not involved in two or more services. MANCOVA was used to determine the relationship among service quality and resilience, risk, and outcomes with four covariates that assessed family and neighborhood environments, history of abuse and neglect, and chronic need. Results indicate that service quality had an effect on resilience, risks, and outcomes. These relationships were mediated quite strongly by the influence of the risks youth faced in their neighborhoods and to a lesser extent by the other three covariates. Of the three dependent variables, risk appeared to be the most consistently influenced by all the covariates, and it also differentiated service experience groups. Results point to the importance of services developing strategies to effectively address risks confronted by youth and also to ensure that when more than one service is involved with youth, consistency in service delivery is achieved.     

Pair programming and secondary school girls’ enjoyment of programming and the subject Information Technology (IT)

This paper reports on a qualitative study that examined how pair programming shapes the experience of secondary school girls taking IT as a subject, with respect to their enjoyment of programming and the subject itself. The study involved six Grade 11 girls who were doing solo programming in Grade 10 and pair programming in their following Grade. The results showed that the girls enjoyed the subject more when programming in pairs due to improved comprehension of the task. They especially enjoyed the socialization and communication brought about by pair programming. The assistance, support, motivation, focus and encouragement they received from partners when stuck or while fixing errors made the programming experience more enjoyable for them. The increased enjoyment brought about by pair programming resulted in the perception of greater learning in the subject IT and also to greater interest in it. It also led to greater persistence in dealing with problems. Pair programming should be implemented right from the start of Grade 10 since it may lead to greater enjoyment of programming and the subject IT in general. The approach may also lead to more girls being attracted to the subject.     

On the interplay between normalisation,bias, and performance of paper impact metrics

We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics’ ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers’ semantic information).In terms of bias, we find that citation counts are always less time biased but always more field biased compared to PageRank. Furthermore, rescaling paper scores by a constant number of similarly aged papers reduces time bias more effectively compared to normalising by calendar years. We also find that percentile citation scores are less field and time biased than mean-normalised citation counts.In terms of performance, we find that time-normalised metrics identify high-impact papers better shortly after their publication compared to their non-normalised variants. However, after 7 to 10 years, the non-normalised metrics perform better. A similar trend exists for the set of high-quality papers where these performance cross-over points occur after 5 to 10 years.Lastly, we also find that personalising PageRank with papers’ citation counts reduces time bias but increases field bias. Similarly, using papers’ associated journal impact factors to personalise PageRank increases its field bias. In terms of performance, PageRank should always be personalised with papers’ citation counts and time-rescaled for citation windows smaller than 7 to 10 years.