Posterpresentatie op Conferentie. Introduction: Classifiers are handshapes (sometimes combined with a specific orientation) that, when combined with the other parameters of movement and location form a ‘verb of motion or location’. There is a limited body of research available on the acquisition of classifiers by children. The available studies have focused on deaf children of deaf (DOD) parents, who are native signers. Results show that classifiers emerge at 3 years and approach an adult like level at the age of 9 (Beal Alvarez & Easterbrooks, 2013). This small study was set out to investigate the production of classifiers in DOH children who acquire Sign Language of the Netherlands. Our expectation was that DOH children produce classifiers, but fail to use them correctly in all instances due to lack of pragmatic control (Slobin et al., 2003). Method: Four children (two girls, two boys) were recruited at a school for the Deaf in The Netherlands (5;10 – 6;8 years). All children were deaf or severely hearing-impaired from birth. Children used (sign supported) Dutch at home and sign language at school and had approximately three years of exposure to sign language. Narratives (Frog-story) were recorded. The recordings were transcribed and analyzed using ELAN-software. Analysis focused on type of classifier (entity and handling) and accuracy in production. Results: The children produced 22 classifiers in total, 20 entity classifiers and 2 handling classifiers. Ten percent of the entity classifiers was incorrect; the handshape to express the entity did not match the handshape frequently selected for that entity. Conclusion: DOH children produce classifiers after three years of exposure to sign language. Errors in classifier production involved errors in handshape selection. This compares to type of errors frequently found for DOD children. Results will be discussed in relation to the iconic and gestural properties of classifiers (Cormier et al., 2012). References: Beal-Alvarez, J.S. & Easterbrooks, S.R. (2013). Increasing children’s ASL classifier production: A multicomponent intervention. American Annals of the Deaf, 158, 311 – 333. Cormier, K., Quinto-Pozos, D., Sevcikova, Z., Schembri, A. (2012). Lexicalisation and de-lexicalisation processes in sign languages: Comparing depicting constructions and viewpoint gestures. Language & Communication, 32, 329 – 348. Slobin, D., Hoiting, N., Kuntze, K., Lindert, R., Weinberg, A. Pyers, J., Anthony, M., Biederman, Y., Thumann, H. (2003). A cognitive/functional perspective on the acquisition of ‘classifiers’. In: Emmorey, K. (Ed.). Perspectives on classifier constructions in sign languages. Lawrence Erlbaum Associates, Mahwah, NJ. Pp 297 – 310.
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Teacher beliefs have been shown to play a major role in shaping educational practice, especially in the area of grammar teaching―an area of language education that teachers have particularly strong views on. Traditional grammar education is regularly criticized for its focus on rules-of-thumb rather than on insights from modern linguistics, and for its focus on lower order thinking. A growing body of literature on grammar teaching promotes the opposite, arguing for more linguistic conceptual knowledge and reflective or higher order thinking in grammar pedagogy. In the Netherlands, this discussion plays an important role in the national development of a new curriculum. This study explores current Dutch teachers’ beliefs on the use of modern linguistic concepts and reflective judgment in grammar teaching. To this end, we conducted a questionnaire among 110 Dutch language teachers from secondary education and analyzed contemporary school textbooks likely to reflect existing teachers’ beliefs. Results indicate that teachers generally appear to favor stimulating reflective judgement in grammar teaching, although implementing activities aimed at fostering reflective thinking seems to be difficult for two reasons: (1) existing textbooks fail to implement sufficient concepts from modern linguistics, nor do they stimulate reflective thinking; (2) teachers lack sufficient conceptual knowledge from linguistics necessary to adequately address reflective thinking.
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The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
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