Evaluating Family Group Conferencing: Towards a meaningful research methodology     

《Child abuse & neglect》2018

There is discussion on the most appropriate research methodology to examine the efficacy of Family Group Conferencing (FGC). Randomised controlled trials (RCTs), despite their pitfalls, are considered by many to be the ‘golden standard’, but the argument is not compelling. In this paper, the theory on programme evaluation is discussed which offers an alternative methodology to study FGC. It is argued that reaching a comprehensive image of truth in the social sciences is never within reach. A RCT is an abstraction of reality, it only provides a partial image of the complex reality of families and the impact that FGC has on safety issues and the quality of their lives. Moreover, the rigour of a study depends heavily on the researcher’s interpretative skills. In studying the efficacy of a complex intervention, such as FGC, it is a challenge to provide a valid and reliable picture of its impact. The context of such a conference, where the lifeworld of families constantly interacts with the system world of professionals, is characterised by multiplicity, polyvalence and interference. The methodology used to examine the efficacy of FGC should meet this ‘interplexitiy’.  相似文献   

Positivity bias in higher education research     

Malcolm Tight 《Higher Education Quarterly》2023,77(2):201-214

Many forms of bias have been identified in higher education research, and in educational and social research in general. This article identifies a further form of bias, positivity bias, and places it in this broader context. Positivity bias is the tendency, in some forms of published higher education research, to only or chiefly report examples of initiatives or innovations that worked and received positive evaluations. Empirical evidence of positivity bias, in the form of an analysis of a sample of published articles from selected higher education journals, is presented and discussed. Suggestions as to the causes of positivity bias and how it might be handled are presented.  相似文献   

Evaluating unfairness of popularity bias in recommender systems: A comprehensive user-centric analysis     

《Information processing & management》2022,59(6):103100

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Who will teach the teachers? Examining implicit bias in the educator workforce     

《Learning and Instruction》2022

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The future of standardised assessment: Validity and trust in algorithms for assessment and scoring     

Cesare Aloisi 《European Journal of Education》2023,58(1):98-110

This article considers the challenges of using artificial intelligence (AI) and machine learning (ML) to assist high-stakes standardised assessment. It focuses on the detrimental effect that even state-of-the-art AI and ML systems could have on the validity of national exams of secondary education, and how lower validity would negatively affect trust in the system. To reach this conclusion, three unresolved issues in AI (unreliability, low explainability and bias) are addressed, to show how each of them would compromise the interpretations and uses of exam results (i.e., exam validity). Furthermore, the article relates validity to trust, and specifically to the ABI+ model of trust. Evidence gathered as part of exam validation supports each of the four trust-enabling components of the ABI+ model (ability, benevolence, integrity and predictability). It is argued, therefore, that the three AI barriers to exam validity limit the extent to which an AI-assisted exam system could be trusted. The article suggests that addressing the issues of AI unreliability, low explainability and bias should be sufficient to put AI-assisted exams on par with traditional ones, but might not go as far as fully reassure the public. To achieve this, it is argued that changes to the quality assurance mechanisms of the exam system will be required. This may involve, for example, integrating principled AI frameworks in assessment policy and regulation.  相似文献   

Opportunities and limitations of using the linguistic category model as a measure of teachers’ implicit stereotypes in Germany     

《Learning and Instruction》2022

The current work focused on how ethnic stereotypes manifest themselves in language. The aim of our research was to investigate whether the Linguistic Category Model (LCM) is applicable to German teachers' language. More precisely, we investigated whether the categories proposed by Semin and Fiedler (1988) and mostly tested in the English-speaking world are applicable to German teachers' language and whether the pattern of Linguistic Intergroup Bias is evident in teachers' language. The analyses of two samples including in- and pre-service teachers (Study 1: N = 124, 76.6% female; Study 2: N = 104, 67.6% female) revealed difficulties in applying the LCM to German teachers' language. Further, neither of the two conducted studies found patterns of Linguistic Intergroup Bias in teachers’ language. We attribute this to a mismatch between the LCM and the German language and discuss the difficulties and limitations of applying the category system to other languages.  相似文献   

Reducing 0s bias in video moment retrieval with a circular competence-based captioner     

《Information processing & management》2023,60(2):103147

The current study addresses the problem of retrieving a specific moment from an untrimmed video by a sentence query. Existing methods have achieved high performance by designing various structures to match visual-text relations. Yet, these methods tend to return an interval starting from 0s, which we named “0s bias”. In this paper, we propose a Circular Co-Teaching (CCT) mechanism using a captioner to improve an existing retrieval model (localizer) from two aspects: biased annotations and easy samples. Correspondingly, CCT contains two processes: (1) Pseudo Query Generation (captioner to localizer), aiming at transferring the knowledge from generated queries to the localizer to balance annotations; (2) Competence-based Curriculum Learning (localizer to captioner), training the captioner in an easy-to-hard fashion guided by localization results, making pairs of the false-positive moment and pseudo query become easy samples for the localizer. Extensive experiments show that our CCT can alleviate “0s bias” with even 4% improvement for existing approaches on average in two public datasets (ActivityNet-Captions, and Charades-STA), in terms of R@1,IoU=0.7. Notably, our method also outperforms baselines in an out-of-distribution scenario. We also quantitatively validate CCT’s ability to cope with “0s bias” by a proposed metric, DM. Our study not only theoretically contributes to detecting “0s bias”, but also provides a highly effective tool for video moment retrieval by alleviating such bias.  相似文献   

刑事庭审实质化视域下法官认知体系的建构          下载免费PDF全文

胡锋 《唐山学院学报》2021,34(1):21-27

刑事审判中的法官认知偏差具有阶段差异性,主要分为事实认知偏差、法律认知偏差和社会认知偏差,存在于证据材料处理、证据证明力认证、案件事实认定和案件裁判四个阶段;隧道视野、印证证明、经验法则、锚定效应、选择性注意和投射效应是法官认知偏差形成的主要影响因素.对法官认知偏差进行有效控制,可从法官自身修正、外部因素革除和程序制度...  相似文献