Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing

In this paper, a systematic multi-objective fuzzy modeling approach is proposed, which can be regarded as a three-stage modeling procedure. In the first stage, an evolutionary based clustering algorithm is developed to extract an initial fuzzy rule base from the data. Based on this model, a back-propagation algorithm with momentum terms is used to refine the initial fuzzy model. The refined model is then used to seed the initial population of an immune inspired multi-objective optimization algorithm in the third stage to obtain a set of fuzzy models with improved transparency. To tackle the problem of simultaneously optimizing the structure and parameters, a variable length coding scheme is adopted to improve the efficiency of the search. The proposed modeling approach is applied to a real data set from the steel industry. Results show that the proposed approach is capable of eliciting not only accurate but also transparent fuzzy models

Using prior information to identify boundaries in disease risk maps

Disease maps display the spatial pattern in disease risk, so that high-risk clusters can be identified. The spatial structure in the risk map is typically represented by a set of random effects, which are modelled with a conditional autoregressive (CAR) prior. Such priors include a global spatial smoothing parameter, whereas real risk surfaces are likely to include areas of smooth evolution as well as discontinuities, the latter of which are known as risk boundaries. Therefore, this paper proposes an extension to the class of CAR priors, which can identify both areas of localised spatial smoothness and risk boundaries. However, allowing for this localised smoothing requires large numbers of correlation parameters to be estimated, which are unlikely to be well identified from the data. To address this problem we propose eliciting an informative prior about the locations of such boundaries, which can be combined with the information from the data to provide more precise posterior inference. We test our approach by simulation, before applying it to a study of the risk of emergency admission to hospital in Greater Glasgow, Scotland

Exploring young students creativity: The effect of model eliciting activities

The aim of this paper is to show how engaging students in real-life mathematical situations can stimulate their mathematical creative thinking. We analyzed the mathematical modeling of two girls, aged 10 and 13 years, as they worked on an authentic task involving the selection of a track team. The girls displayed several modeling cycles that revealed their thinking processes, as well as cognitive and affective features that may serve as the foundation for a methodology that uses model-eliciting activities to promote the mathematical creative process

Wrongful Convictions by Police-Induced False Confessions

Wrongful convictions have two main negative effects on society: (1) innocent people are imprisoned, and (2) the real perpetrators are allowed to wander the streets. In order to analyze this issue, this presentation discusses police interrogation tactics (specifically when using the Reid Technique), the association between police interrogation tactics and false confessions, the association between false confessions and wrongful convictions, ways in which wrongful convictions affect communities and their members, and possible ways to mitigate this problem. Although current police interrogation tactics can be useful at eliciting confessions, interrogation methods must be reformed in light of evidence that police-induced false confessions occur and result in wrongful convictions

HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.Comment: Published in the proceedings of WWW'1

Factors revealed while posing mathematical modelling problems by mathematics student teachers

The purpose of this study is to reveal factors considered by mathematics student teachers while posing modelling problems. The participants were twenty-seven mathematics student teachers and posed their modelling problems within their groups. The data were obtained from the modelling problems posed by the participants, their solutions on these problems and the groups’ reflective diaries regarding their problem posing and solution processes. The data were analyzed by using content analysis and the codes were constructed according to the problems’ contents. The participants' diaries were examined in terms of generated codes and the expressions supporting/relating the codes were determined. While designing the problems, the participants considered the factors such as being interesting, understandable, appropriateness to real life and modelling process, model construction, and usability of different mathematical concepts. Their solutions were generally handled in terms of usage of the mathematical statements, appropriateness to the modelling process and being meaningful for real life. Modelling training should be provided to enable the student teachers to develop modelling problems and their designs should be examined and the feedbacks should be given. © 2018 Eurasian Society of Educational Research. All Rights Reserved

Operationalizing Individual Fairness with Pairwise Fair Representations

We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation (PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including human judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable.Comment: To be published in the proceedings of the VLDB Endowment, Vol. 13, Issue.

Negotiation of Meaning in the English as a Foreign Language Classroom Interaction

Studi ini difokuskan pada negosiasi makna di dalam bahasa Inggris sebagai interaksi kelas bahasa asing. Dua pertanyaan penelitiannya adalah: 1) Tipe-tipe transaksi, pertukaran,perpindahan dan tindakan apakah yang terjadi pada negosiasi makna dalam bahasa Inggris sebagai sebuah Interaksi Kelas Bahasa Asing antara dosen dan mahasiswa? 2) Sampai padatingkatan apakah dosen dan mahasiswa memerankan peranan di dalam proses negosiasi makna dalam bahasa Inggris sebagai sebuah Interaksi Kelas Bahasa Asing? Penelitian ini menggunakan metode kualitatif-deskriptif. Subjek studi ini adalah dosen dan mahasiswa semester enam padaProgram Studi Pendidikan Bahasa Inggris pada kelas Metodologi Penelitian. Analisis ini pada dasarnya didasarkan pada analisis wacana kelas yang diajukan oleh Sinclair dan Coultharddengan sistem yang disebut: transaksi, pertukaran, perpindahan, dan tindakan. Hasil dari studiini menunjukkan bahwa (a) tiga tipe transaksi pokok yang terjadi pada wacana kelas EFL adalah; 1) melaporkan transaksi sebanyak 44 kali (45,36%), 2) memunculkan transaksisebanyak 35 kali (35.05 %), dan 3) mengarahkan transaksi sebanyak 19 kali (19.59 %). (b) Tipe-tipe pertukaran yang terjadi pada wacana kelas EFL meliputi 14 jenis pertukaran, yangterjadi sebanyak 128 kali, tetapi yang paling sering terjadi adalah membatasi (boundary), yaitu sebanyak 51 kali (39.84 %) dari keseluruhan kejadian pertukaran, sedangkan memperkuat (reinforce) dan menerima (accept) menempati posisi terendah, yaitu satu kali (0.78 %) dari keseluruhan kejadian pertukaran. (c) Tipe-tipe perpindahan yang terjadi pada wacana kelas EFL di dalam kelas Metode Penelitian adalah: 1) Perpindahan Awal (pembukaan) terjadi sebanyak 36 kali (37.5 %), 2) Perpindahan Respon (memberikan jawaban) terjadi sebanyak 34 kali (35.42 %), dan Perpindahan Umpan-Balik (tindak lanjut) terjadis sebanyak 26 kali (27.08 %).(d) Pada tipe-tipe tindakan yang terjadi pada wacana kelas EFL, terdapat 22 jenis tindakan yang terjadi, sebanyak 1.106 kali, tetapi yang paling sering terjadi adalah yang membenarkan(acknowledge), yaitu sebanyak 238 kali (21.52 %) dari seluruh kejadian tindakan, sementara memeriksa (check) menempati posisi terendah yaitu 3 kali (0.27 %) dari seluruh kejadiantindakan. Peran dosen dan mahasiswa di dalam proses negosiasi makna antara lain: 1) proses ritual yang dilakukan oleh dosen, sebanyak 8 kali (53.33 %), proses ritual yang dilakukan oleh mahasiswa, sebanyak 7 kali (46.67 %); sementara ritual negosiasi yang dilakukan sebanyak 12kali dibagi ke dalam Data I sebanyak 6 kali atau 50 % dan Data II sebanyak 6 kali atau 50 % pula. Diharapkan bahwa hasil dari studi ini akan memberikan informasi kepada para dosenbahsa Inggris di Indonesia sebagai konteks EFL mengenai beberapa strategi yang dapat digunakan untuk mengoptimalkan makna negosiasi antara dosen dan mahasiswa di dalam wacana kelas. Partisipasi mahasiswa di dalam wacana kelas EFL tergantung kepada bagaimana dosen menciptakan situasi, dengan menggunakan metode-metode yang tepat, karakteristik mahasiswa, serta alat-alat instruksi pilihan serta tidak didominasi oleh dosen