Aligning the topic of FCA with existing module learning outcomes

Although Formal Concept Analysis is worthy of study on computing courses, it is not always possible or practical to dedicate a whole module to it. It may, however, fit into an existing module as a topic but require some careful design of teaching and assessment activities to properly align it to the intended learning outcomes of the module. This paper describes and evaluates a three year project to align the teaching and assessment of FCA with the learning outcomes of a final-year undergraduate Smart Applications module at Sheffield Hallam University. Biggs' constructive alignment was used, incorporating an adapted version of Yin's case study research method, in an iterative process; progressively modifying teaching and assessment activities to align them more closely with the prescribed learning outcomes. The process involved examining conclusions made by students, from carrying out FCA case study assignments, to draw cross-case conclusions about the learning outcomes achieved, and how they deviated from the prescribed ones. These cross-case conclusions were used to feed back into the design of learning and assessment activities for the next delivery of the module. After three cycles, the learning outcomes achieved closely matched the prescribed learning outcomes of the module

Proučavanje asocijacija u multilingualnih osoba: međujezično istraživanje

In the literature on mental lexicon, associations are used as a way to inspect that elusive human mechanism. Researchers have until recently mostly opted for studies based on monolingual participants, but language and, therefore, cultural communities are nowadays perceived as “melting pots” and they are mostly multilingual due to different cultural backgrounds of their members. This thesis aims to explore associations and mental lexicon organisation of multilingual speakers of Croatian, English and Russian. Association questionnaires have been used to collect data which has further been statistically analysed and explained in terms of associative fields and conceptualisation overlaps caused by typological closeness of languages at large and their status in users’ repertoires. Based on a review of the literature on linguistic culturology, the Slavic etymology and tradition that Croatian and Russian languages share shapes the way in which speakers form their linguistic picture of the world. Analysis of the responses has shown that the conceptual categories in participants’ languages are often mediated by the L1 concept and that many variables, such as e.g. participants’ language proficiency and word-related variables have an effect on the answers in the three languages. Due to a small sample of participants the results obtained are only tentative and further research is needed.В литературе по ментальной лексике ассоциации используются как решение для изучения этого неуловимого человеческого механизма. До недавнего времени исследователи в основном занимались исследованиями, основанными на данных одноязычных респондентов, но в настоящее время сообщества воспринимаются как «плавильные котлы», и они в основном многоязычны из-за разного культурного происхождения своих членов. Данные тезисы направлены на изучение ассоциаций и организацию ментальной лексики многоязычных носителей хорватского, английского и русского языков. Анкеты ассоциаций использовались для сбора данных, которые затем подвергались статистическому анализу и объяснению с точки зрения ассоциативных полей и концептуальных совпадений, вызванных типологической близостью языков и их статуса в репертуаре респондентов. Опираясь на литературу по лингвокультурологии, общая славянская этимология и традиции, которыми обладают хорватский и русский языки, определяют способ, которым говорящие на этих языках формируют свою языковую картину мира. Анализ ответов показал, что концептуальные категории в языках участников часто опосредуются концепцией L1 и что многие факторы, как например, уровень владения языком и факторы, связанные со словами-стимулами, влияют на ответы на трех языках. Но размер выборки, использованный в этом исследовании, является лишь ориентировочным. Поэтому необходимы дальнейшие исследования.U literaturi na temu mentalnog leksikona asocijacije se javljaju najboljim rješenjem za ispitivanje tog ljudskog mehanizma. Istraživači su se sve do nedavno opredjeljivali za istraživanja zasnovanima na monolingualnim sudionicima, no jezične, pa samim time i kulturne zajednice se danas percipiraju kao „melting pot“ i najčešće su multilingualne zbog različitih kulturoloških pozadina svojih članova. Cilj je ovog diplomskog rada istražiti asocijacije i organizaciju mentalnog leksikona višejezičnih govornika hrvatskog, engleskog i ruskog jezika. Za skupljanje podataka korišteni su upitnici asocijacija koji su se zatim statistički analizirali i objasnili u okvirima asocijativnih polja i konceptualnih podudaranja uzrokovanih tipološkom bliskošću proučavanih jezika, kao i statusom tih jezika u njihovim repertoarima. Na temelju lingvokulturološke teorije, slavenska etimologija i tradicija, koju ruski i hrvatski dijele, uvjetuje način na koji govornici oblikuju svoju jezičnu sliku svijeta. Analiza odgovora pokazala je da su sličnosti u konceptualnim kategorijama sudionika često pod utjecajem materinskog jezika. Nadalje, mnogi faktori, poput razine znanja jezika sudionika i faktori vezani uz riječi-stimule utječu na odgovore u trima jezicima. Zbog veličine uzorka korištenog u ovom istraživanju, rezultati koji su dobiveni smatraju se samo indikativnima pa je, prema tome, potrebno daljnje istraživanje

An Exploratory Analysis of Twitter Keyword-Hashtag Networks and Knowledge Discovery Applications

The emergence of social media has impacted the way people think, communicate, behave, learn, and conduct research. In recent years, a large number of studies have analyzed and modeled this social phenomena. Driven by commercial and social interests, social media has become an attractive subject for researchers. Accordingly, new models, algorithms, and applications to address specific domains and solve distinct problems have erupted. In this thesis, we propose a novel network model and a path mining algorithm called HashnetMiner to discover implicit knowledge that is not easily exposed using other network models. Our experiments using HashnetMiner have demonstrated anecdotal evidence of drug-drug interactions when applied to a drug reaction context. The proposed research comprises three parts built upon the common theme of utilizing hashtags in tweets. 1 Digital Recruitment on Twitter. We build an expert system shell for two different studies: (1) a nicotine patch study where the system reads streams of tweets in real time and decides whether to recruit the senders to participate in the study, and (2) an environmental health study where the system identifies individuals who can participate in a survey using Twitter. 2 Does Social Media Big Data Make the World Smaller? This work provides an exploratory analysis of large-scale keyword-hashtag networks (K-H) generated from Twitter. We use two different measures, (1) the number of vertices that connect any two keywords, and (2) the eccentricity of keyword vertices, a well-known centrality and shortest path measure. Our analysis shows that K-H networks conform to the phenomenon of the shrinking world and expose hidden paths among concepts. 3 We pose the following biomedical web science question: Can patterns identified in Twitter hashtags provide clinicians with a powerful tool to extrapolate a new medical therapies and/or drugs? We present a systematic network mining method HashnetMiner, that operates on networks of medical concepts and hashtags. To the best of our knowledge, this is the first effort to present Biomedical Web Science models and algorithms that address such a question by means of data mining and knowledge discovery using hashtag-based networks

Bilingual Word Association Networks

Bilingual word association networks can be beneficial as a tool in foreign language education because they show relationships among cognate words of different languages and correspond to structures in the mental lexicon. This paper discusses possible technologies that can be used to generate and represent word association networks