https://orcid.org/0000-0003-4607-7305
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Abstract
Grammatical information, embedded at the word level, makes the acquisition of morphologically rich languages quite complex as well as the language exercise generation process for teachers. This study introduces a new gamification approach for complex morphology learning and aims to analyze the students’ perceptions towards it. In this approach, the morphology components and their interactions are gradually introduced to the learners within a gamified environment through automatically generated exercises. Although not specific to any language, the approach has been applied to Turkish which is a strong representative of morphologically rich languages. The study was conducted for three weeks with international students in an introductory level Turkish language course via a mobile application developed using finite-state transducer technology to model morphology. Questionnaires, e-journals and semi-structured interviews were employed to examine the perceptions and experiences of the students in terms of perceived efficacy, system usage, engagement, loyalty, perceived enjoyment, attitude, and willingness to recommend. The findings of the study revealed that the students had positive perceptions towards the proposed approach and found it effective for their learning process. The approach is considered to fill an important gap in grammar exercises for learning morphologically rich languages.
Acknowledgment
The authors would like to thank Istanbul Bilgi University and all the participants for allowing the researchers to conduct this study, Mehmet Yakuphan Bilgiç for acting in the app development team, Doruk Eryiğit for software testing and James Christopher Lawson for proofreading. The second author had been funded by the ITU-Turkcell Researcher Funding Program. Finally, the authors would like to thank the three anonymous reviewers for insightful comments and suggestions that helped them improve the final version of the article.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on Contributors
Gülşen Eryiğit is Associate Professor at ITU Faculty of Computer & Informatics. Her research focuses on Turkish Natural Language Processing (NLP). She is the founding member and the head of the ITU NLP Research Group and the coordinator of the ITU Language Technologies and Social Robotic Laboratory. She is the builder of the ITU Turkish NLP Pipeline (tools.nlp.itu.edu.tr) servicing many academic studies in the field.
Fatih Bektaş is an MS student at ITU computer engineering department and a researcher at ITU NLP Research Group.
Ubey Ali is a graduate of ITU computer engineering department and a member of ITU NLP Research Group
Bihter Dereli is a PhD student in the field of Teaching Turkish as a Foreign Language. Her bachelor’s and master’s degrees are from Boğaziçi University, Department of English Language and Literature and Turkish Language and Literature respectively. She has been teaching Turkish Language I and II, Turkish Story and Novel and Turkish to Foreigners at the Turkish Language Unit of Bilgi University since 2001.
Notes
1 The listed constructs are adapted from Morschheuser, Hamari, Koivisto, and Maedche (Citation2017); Morschheuser, Hamari, and Maedche (Citation2019)
2 A3pl: third person plural, A3sg: third person singular, P3pl: third person plural possessive, P2sg: second person singular possessive, P3sg: third person singular possessive, Dat: dative (Eryiğit, Citation2014; Eryiğit, Nivre, & Oflazer, Citation2008).
3 It is a tradition in Turkish NLP studies to depict the derivational boundaries with a ˆDB (Eryiğit, Citation2014; Eryiğit et al., Citation2008).
5 Clue words are the words that the lecturer uses to indicate the angle to take when you answer a question.
8 In case of missed characters the neighbouring characters are underlined.
9 Picker is an input entry type in mobile apps; mainly a slider choice list.
11 The Covid-19 Pandemic, which emerged at the time of writing this article, necessitated the transition to online education in many parts of the world.
12 The numbers were calculated according to the usage counts of each mode’s subparts; i.e., verbs and nouns tabs in the education module, and all the games under the game module.