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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 6...52 Hits 21 – 40 of 1.032 21 SimLex-999 Slovenian translation SimLex-999-sl 1.0 Pollak, Senja; Vulić, Ivan; Pelicon, Andraž. - : University of Ljubljana, 2021 BASE Show details 22 Evaluation of Sentence Representations in Semantic Text Similarity Tasks ; Utvärdering av meningsrepresentation för semantisk textlikhet Balzar Ekenbäck, Nils. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021 BASE Show details 23 The unexplored role of handshape similarity in processing numbers on the hands ... Berteletti, Ilaria; Sullivan, SaraBeth J.; Lancaster, Lucas. - : PsychOpen GOLD, 2021 BASE Show details 24 “Alignment is All You Need”: Analyzing Cross-Lingual Document Similarity for Domain-Specific Applications ... Dutta, Sourav. - : Leibniz Universität Hannover (LUH),L3S Research Center,CLEOPATRA ITN, 2021 BASE Show details 25 Korpuslinguistik in der Rechtswissenschaft. Eine webbasierte Analyseplattform für EuGH-Entscheidungen ... Mielke, Bettina; Wolff, Christian. - : Universität Regensburg, 2021 BASE Show details 26 Idioms Comprehension in View of Degrees of Transparency and Contributing Factors: A Contrastive Study ... Mohamad Mansour Al-Ajalein, Yazan Shaker Almahameed. - : Zenodo, 2021 BASE Show details 27 Idioms Comprehension in View of Degrees of Transparency and Contributing Factors: A Contrastive Study ... Mohamad Mansour Al-Ajalein, Yazan Shaker Almahameed. - : Zenodo, 2021 BASE Show details 28 Text analysis using colexification networks ... : Textanalyse mit colexification Netzwerken ... Gander, Armin. - : TU Wien, 2021 BASE Show details 29 Acoustic versus Mental Phonological Similarity (AMPS) ... AuBuchon, Angela. - : Open Science Framework, 2021 BASE Show details 30 phonological-similarity-in-complex-span ... Chow, Michael. - : Open Science Framework, 2021 BASE Show details 31 Designing Secure and Usable Wake Words; Reclaiming the Road: How Agendas for American Car-free Cities are Advancing in the COVID Era ... : University of Virginia, 2021 BASE Show details 32 Effects of similarity on speakers’ production of referring expressions ... Zhou, Yangzi. - : The University of Edinburgh, 2021 BASE Show details 33 A cross-linguistic investigation of retroactive similarity-based interference in sentence comprehension. ... Mertzen, Daniela. - : Open Science Framework, 2021 BASE Show details 34 I Explain, You Collaborate, He Cheats: An Empirical Study with Social Network Analysis of Study Groups in a Computer Programming Subject Beatriz Barros; Ricardo Conejo; Amparo Ruiz-Sepulveda; Francisco Triguero-Ruiz In: Applied Sciences ; Volume 11 ; Issue 19 (2021) Abstract: Students interact with each other in order to solve computer science programming assignments. Group work is encouraged because it has been proven to be beneficial to the learning process. However, sometimes, collaboration might be confused with dishonest behaviours. This article aimed to quantitatively discern between both cases. We collected code similarity measures from students over four academic years and analysed them using statistical and social network analyses. Three studies were carried out: an analysis of the knowledge flow to identify dishonest behaviour, an analysis of the structure of the social organisation of study groups and an assessment of the relationship between successful students and social behaviour. Continuous dishonest behaviour in students is not as alarming as many studies suggest, probably due to the strict control, automatic plagiarism detection and high penalties for unethical behaviour. The boundary between both is given by the amount of similar content and regularity along the course. Three types of study groups were identified. We also found that the best performing groups were not made up of the best individual students but of students with different levels of knowledge and stronger relationships. The best students were usually the central nodes of those groups. Keyword: dishonest behaviour; groups collaborative learning; higher education; knowledge sharing; learning communities; social network analysis; source-code similarity URL: https://doi.org/10.3390/app11199328 BASE Hide details 35 Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach Anwar Ali Yahya; Adel A. Sulaiman; Aisha Mousa Mashraqi... In: Applied Sciences ; Volume 11 ; Issue 20 (2021) BASE Show details 36 A Unifying Framework for Analysis of Spatial-Temporal Event Sequence Similarity and Its Applications Fuyu Xu; Kate Beard In: ISPRS International Journal of Geo-Information ; Volume 10 ; Issue 9 (2021) BASE Show details 37 Bidirectional String Anchors: A New String Sampling Mechanism ... Loukides, Grigorios; Pissis, Solon P.. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021 BASE Show details 38 СРАВНИТЕЛЬНЫЙ АНАЛИЗ ЛЮБОВНОЙ ЛИРИКИ А.А. ФЕТА И СЮЙ ЧЖИМО ... : COMPARATIVE ANALYSIS OF LOVE LYRICS OF A.A. FET AND XU ZHIMO ... Дай Мэнцзе. - : Мир науки, культуры, образования, 2021 BASE Show details 39 The Influence of Cross-Linguistic Similarity and Language Background on Writing to Dictation Paolieri, Daniela; Bajo Molina, María Teresa; Rossi, Eleonora. - : Frontiers Research Foundation, 2021 BASE Show details 40 Avoiding conflict : when speaker coordination does not require conceptual agreement Kabbach, Alexandre; Herbelot, Aurélie In: ISSN: 2624-8212 ; Frontiers in Artificial Intelligence, Vol. 3, No 523920 (2021) P. 24 (2021) BASE Show details Page: 1 2 3 4 5 6...52

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