MétaCan
Menu
Retour à la cohorte
Enregistrement W2238251647

How do female and male students work with an online vocabulary program

2010· article· en· W2238251647 sur OpenAlexaffabout
Ulf Schuetze

Notice bibliographique

RevueSociety for Information Technology & Teacher Education International Conference · 2010
Typearticle
Langueen
DomainePsychology
ThématiqueSecond Language Acquisition and Learning
Établissements canadiensUniversity of Victoria
Organismes subventionnairesnon disponible
Mots-clésVocabularyWork (physics)PsychologyComputer scienceLinguisticsEngineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This paper reports on a large-scale study carried out at the University of Victoria on second language vocabulary acquisition. The research investigated intentional vocabulary learning in combination with the use of information technology as suggested by Schmitt (2008). The research questions were how female and male learners used an online vocabulary program and if this was related to their learning outcome. A total of 186 first-year German as a foreign language students (104 female, 82 male) participated in the study over two semesters. The analysis showed that the male students used the program significantly less than the female students in the second semester. Although the female students slightly outperformed the male students on the vocabulary quizzes, the differences were not statistically significant. 1 Context of the study Naturally, learners have individual preferences when it comes to studying a foreign language. Researchers are interested in questions such as what motivates the learners and how they can be motivated. In her book on gender differences in language learning, Chavez (2001) describes how female and male students interact differently in the second language classroom, and how they prefer different task-types, topics and feedback. However, little is said about information technologies (IT) in this context, an area that has seen an exponential rise in the last ten years. In today’s foreign/second language classroom, software programs, online applications, or internet learner platforms are often used by instructors to enhance the learning experience. The question is, though, do they and if so, do they advantage or disadvantage female or male students? Most of the studies on motivation towards language learning, in particular, using IT in the second language classroom, have produced mixed outcomes: while some reported gender differences, others found no evidence of such differences. The study of gender differences in relation to vocabulary acquisition, in particular on intentional second language vocabulary learning facilitated by a computer program, has more or less gone unnoticed. This article therefore aims to look into this area with a study that was carried out in several sections of a beginner’s German program during one academic year at the University of Victoria in Canada. The program was specially designed so that the students could review words five times over a period of ten days and the following questions could be addressed: whether the students actually used the potential of the program, whether the female and male students used it similarly, and what impact such uses had on their learning outcome. How do Female and Male Students work with an Online Vocabulary Program? 211 1.1 Motivation and anxiety towards a foreign or second language Studies on motivation in relation to differences between female and male students in foreign or second language acquisition have produced mixed results. A large-scale survey on motivation with almost 5000 participants carried out in Hungary by Dornyei and Clement (2001) using Gardner’s socio-educational model reported that female students scored higher than male students in all aspects of motivation including integrative and instrumental factors. The target languages were English, German, French, Italian and Russian. A survey of almost 500 students carried out by Mori and Gobel (2006) in Japan in an English as a Foreign Language (EFL) classroom also used Gardner’s model, yet did not confirm these results. One explanation might be the different cultural settings of these studies, as the cultural heritage and societal norms might play a role in motivation. In her book on gender, Chavez (2001) describes how male students are often less engaged in the second language classroom. They don’t interact as much with peers as female students do and are skeptical towards feedback received from peers. However, this behavior does not necessarily affect their learning outcome. Female students, on the other hand, are more willing to communicate, in particular with native speakers, and engage in discussions. 1.2 Information technology and the second language classroom The next question is in what way female and male students handle information technology in the second language classroom and if there is a link to motivational factors. A large-scale study carried out in the United Kingdom based on 427 surveys and 85 audio logs reported that language students appreciate using Web 2.0 technology that allows them to create their own content (Conole, 2008). It was observed that language learners used this technology in a reflective, critical manner and integrated online sources with print materials. A similar result was reported by Lee (2005) in a study carried out at the University of New Hampshire surveying advanced Spanish students. Not all studies are that positive. Winke and Goertler (2008) surveyed 911 firstand second-year undergraduate French, German and Spanish language students at Michigan State University, and found that many students do not have the skills necessary to engage in the specialized nature of computer-assisted language learning tasks because these tasks are different than the everyday use of e-mail or Facebook. A study by Stracke (2007) carried out in Australia echoed these findings and added that some language students reject the computer as a medium of language learning because it is impersonal. In some cases, students showed high levels of anxiety to study in a hybrid or blended language learning context (Ushida, 2005). Ushida used Gardner’s socioeducational model of second language acquisition for her study finding that students who were motivated to engage in the online component of the course showed higher scores on tests than those who had high levels of anxiety. Interestingly, those differences were not statistically significant. Ushida also reported the biggest disadvantage of such courses was the reduced interaction between teachers and students. Not many studies have investigated gender differences regarding attitudes, motivation or performance using computers. Based on an investigation of 850 secondary school students (444 males, 406 females) in Ontario using different online learning objects covering concepts in biology, history, chemistry, general science, geography, mathematics, and physics, Kay and Knaack (2007) found no marked individual differences between female and male students. In the context of second language learning, studies of this kind are surprisingly hard to find. One explanation might be that no clear picture has emerged regarding motivation comparing female and male students so far. 1.3 Vocabulary acquisition The process of acquiring a word in a second language has been investigated by several researchers. One area of investigation is the context, in which words are acquired by. Oxford (2003) points out that one would expect that certain tasks favor certain learner types. In vocabulary learn-

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,754
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,354
Écart entre enseignants0,332 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2010
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueSociety for Information Technology & Teacher Education International ConferenceMême sujetSecond Language Acquisition and LearningTravaux en français237 207