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Personalized word learning for university students: a profile-based method for e-learning systems

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Abstract

It is widely acknowledged that the acquisition of vocabulary is the foundation of learning English. With the rapid development of information technologies in recent years, e-learning systems have been widely adopted for English as a Second Language (ESL) Learning. However, a limitation of conventional word learning systems is that the prior vocabulary knowledge of learners is not well captured. Understanding the prior knowledge of learners plays a key role in providing personalized learning, which many studies suggest is a successful learning paradigm for vocabulary acquisition, one that aims to optimize instructional approaches and paces by catering to individual learning needs. A powerful learner profile model which can represent learner’s prior knowledge is therefore important for word learning systems to better facilitate personalized learning. In this article, we investigated various methods to establish learner profiles and attempted to determine the optimal method. To verify the effectiveness of personalized word learning supported by the proposed model, ESL students from several universities participated in this study. The empirical results showed that the proposed learner profile model can better facilitate vocabulary acquisition compared with other baseline methods.

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Notes

  1. https://www.twinword.com/blog/how-to-check-english-word-difficulty/.

  2. http://language.tiu.ac.jp/flc/index.html.

References

  • Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers & Education, 125, 413–428.

    Article  Google Scholar 

  • Baker, R. S. J., & Clarke-Midura, J. (2013). Predicting successful inquiry learning in a virtual performance assessment for science. In International conference on user modeling, adaptation, and personalization (pp. 203–214). Springer

  • Barbeau, E. B., Chai, X. J., Chen, J.-K., Soles, J., Berken, J., Baum, S., et al. (2017). The role of the left inferior parietal lobule in second language learning: An intensive language training fmri study. Neuropsychologia, 98, 169–176.

    Article  Google Scholar 

  • Bedogni, L., Di Felice, M., & Bononi, L. (2012). By train or by car? detecting the user’s motion type through smartphone sensors data. In 2012 IFIP Wireless days (wd) (pp. 1–6). IEEE.

  • Bray, B., & McClaskey, K. (2015). Personalization vs. differentiation vs. individualization report (pdi) v3. Viitattu, 16:2015.

  • Brusilovsky, P., & Henze, N. (2007). Open corpus adaptive educational hypermedia. In The adaptive web (pp. 671–696). Springer.

  • Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In The adaptive web (pp. 3–53). Springer.

  • Brusilovsky, P. (2003). Adaptive navigation support in educational hypermedia: The role of student knowledge level and the case for meta-adaptation. British Journal of Educational Technology, 34(4), 487–497.

    Article  Google Scholar 

  • Bulger, M. (2016). Personalized learning: The conversations were not having. Data and Society, 22.

  • Campbell, A., Choudhury, T., Hu, S., Lu, H., Mukerjee, M. K., Rabbi, M., et al. (2010). Neurophone: Brain–mobile phone interface using a wireless EEG headset. In Proceedings of the second ACM SIGCOMM workshop on networking, systems, and applications on mobile handhelds (pp. 3–8). ACM.

  • Chen, C.-M., & Chung, C.-J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624–645.

    Article  Google Scholar 

  • Chen, C.-M., Lee, H.-M., & Chen, Y.-H. (2005). Personalized e-learning system using item response theory. Computers & Education, 44(3), 237–255.

    Article  Google Scholar 

  • Chen, C.-M., & Li, Y.-L. (2010). Personalised context-aware ubiquitous learning system for supporting effective english vocabulary learning. Interactive Learning Environments, 18(4), 341–364.

    Article  Google Scholar 

  • Chen, C.-M., Liu, C.-Y., & Chang, M.-H. (2006). Personalized curriculum sequencing utilizing modified item response theory for web-based instruction. Expert Systems with Applications, 30(2), 378–396.

    Article  Google Scholar 

  • Cochrane, T. D. (2010). Exploring mobile learning success factors. Research in Learning Technology, 18(2), 133–148.

    Article  Google Scholar 

  • Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.

    Google Scholar 

  • Downes, S. (2010). Learning networks and connective knowledge. In Collective intelligence and e-learning 2.0: Implications of web-based communities and networking (pp. 1–26). IGI Global.

  • Folse, K. S. (2006). The effect of type of written exercise on l2 vocabulary retention. TESOL Quarterly, 40(2), 273–293.

    Article  Google Scholar 

  • Friesner, T., & Hart, M. (2005). Learning logs: Assessment or research method. The Electronic Journal of Business Research Methodology, 3(2), 117–122.

    Google Scholar 

  • Fu, Q.-K., & Hwang, G.-J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers & Education, 119, 129–143.

    Article  Google Scholar 

  • Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S. (2014). Technologies for foreign language learning: A review of technology types and their effectiveness. Computer Assisted Language Learning, 27(1), 70–105.

    Article  Google Scholar 

  • Gómez, S., Zervas, P., Sampson, D. G., & Fabregat, R. (2014). Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of King Saud University-Computer and Information Sciences, 26(1), 48.

    Article  Google Scholar 

  • Gu, P. Y. (2003). Vocabulary learning in a second language: Person, task, context and strategies. TESL-EJ, 7(2), 1–25.

    Google Scholar 

  • Hammond, J., & Gibbons, P. (2005). What is scaffolding. Teachers Voices, 8, 13.

    Google Scholar 

  • Hsu, C.-K., Hwang, G.-J., & Chang, C.-K. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327–336.

    Article  Google Scholar 

  • Huang, Y.-M., Huang, Y.-M., Huang, S.-H., & Lin, Y.-T. (2012). A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use. Computers & Education, 58(1), 273–282.

    Article  Google Scholar 

  • Hübscher, R. (2000). Logically optimal curriculum sequences for adaptive hypermedia systems. In International conference on adaptive hypermedia and adaptive web-based systems (pp. 121–132). Springer.

  • Hulstijn, J. H., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51(3), 539–558.

    Article  Google Scholar 

  • Hu, H. M., & Nassaji, H. (2016). Effective vocabulary learning tasks: Involvement load hypothesis versus technique feature analysis. System, 56, 28–39.

    Article  Google Scholar 

  • Jeong, H.-Y., Choi, C.-R., & Song, Y.-J. (2012). Personalized learning course planner with e-learning dss using user profile. Expert Systems with Applications, 39(3), 2567–2577.

    Article  Google Scholar 

  • Joachims, T. (2002). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 133–142). ACM.

  • Jones, K. S. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21.

    Article  Google Scholar 

  • Kaya, T., & Bicen, H. (2016). The effects of social media on students behaviors; Facebook as a case study. Computers in Human Behavior, 59, 374–379.

    Article  Google Scholar 

  • Keating, G. D. (2008). Task effectiveness and word learning in a second language: The involvement load hypothesis on trial. Language Teaching Research, 12(3), 365–386.

    Article  Google Scholar 

  • Khosrowabadi, R., Quek, C., Ang, K. K., & Wahab, A. (2014). ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal. IEEE Transactions on Neural Networks and Learning Systems, 25(3), 609–620.

    Article  Google Scholar 

  • Koć-Januchta, M., Höffler, T., Thoma, G.-B., Prechtl, H., & Leutner, D. (2017). Visualizers versus verbalizers: Effects of cognitive style on learning with texts and pictures-an eye-tracking study. Computers in Human Behavior, 68, 170–179.

    Article  Google Scholar 

  • Lin, C. F., Yeh, Y., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199–210.

    Article  Google Scholar 

  • Loucky, J. P. (2012). Designing distance learning tasks to help maximize vocabulary development. International Journal of Virtual and Personal Learning Environments (IJVPLE), 3(2), 35–58.

    Article  Google Scholar 

  • Martins, C., Faria, L., Carvalho, C. V. D., & Carrapatoso, E. (2008). User modeling in adaptive hypermedia educational systems. Educational Technology & Society, 11(1), 194–207.

    Google Scholar 

  • Maseleno, A., Sabani, N., Huda, M., Ahmad, R., Jasmi, K. A., & Basiron, B. (2018). Demystifying learning analytics in personalised learning. International Journal of Engineering & Technology, 7(3), 1124–1129.

    Article  Google Scholar 

  • McLoughlin, C. E. (2013). The pedagogy of personalised learning: exemplars, MOOCS and related learning theories. In EdMedia: World conference on educational media and technology (pp. 266–270). Association for the Advancement of Computing in Education (AACE).

  • Mobasher, B. (2007). Data mining for web personalization. In The adaptive web (pp. 90–135). Springer.

  • Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in facebook and its application to e-learning. Computers in Human Behavior, 31, 527–541.

    Article  Google Scholar 

  • Oxford, R. L., & Scarcella, R. C. (1994). Second language vocabulary learning among adults: State of the art in vocabulary instruction. System, 22(2), 231–243.

    Article  Google Scholar 

  • Papanikolaou, K. A., Magoulas, G. D., & Grigoriadou, M. (1999). A connectionist approach for adaptive lesson presentation in a distance learning course. In Proceedings of international joint conference on neural networks (Cat. No. 99CH36339), IJCNN’99 (Vol. 5, pp. 3522–3526). IEEE.

  • Paul Nation and Robert Waring. (1997). Vocabulary size, text coverage and word lists. Vocabulary: Description, Acquisition and Pedagogy, 14, 6–19.

    Google Scholar 

  • Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015). Towards an intelligent framework for multimodal affective data analysis. Neural Networks, 63, 104–116.

    Article  Google Scholar 

  • Prince, P. (1996). Second language vocabulary learning: The role of context versus translations as a function of proficiency. The Modern Language Journal, 80(4), 478–493.

    Article  Google Scholar 

  • Rahmani, F., Sobhani, S., & Hadi Aarabi, M. (2017). Sequential language learning and language immersion in bilingualism: Diffusion MRI connectometry reveals microstructural evidence. Experimental Brain Research, 235(10), 2935–2945.

    Article  Google Scholar 

  • Ramakers, R., Vanacken, D., Luyten, K., Coninx, K., & Schöning, J. (2012). Carpus: A non-intrusive user identification technique for interactive surfaces. In Proceedings of the 25th annual ACM symposium on user interface software and technology (pp. 35–44). ACM.

  • Reeves, T. C., & Reeves, P. M. (1997). Effective dimensions of interactive learning on the world wide web. In Web-based instruction (p. 63).

  • Romrell, D., Kidder, L., & Wood, E. (2014). The SAMR model as a framework for evaluating mLearning. Online Learning Journal, 18(2), 1–15.

    Google Scholar 

  • Schmitt, N. (2008). Instructed second language vocabulary learning. Language Teaching Research, 12(3), 329–363.

    Article  Google Scholar 

  • Siemens, G. (2005). Connectivism: A learning theory for the digital age. Instructional Technology and Distance Education, 2(1), 3–10.

    Google Scholar 

  • Subramanya, S. R. (2014). Mobile apps as supplementary educational resources. International Journal of Advances in Management, Technology & Engineering Sciences, 9, 38–43.

    Google Scholar 

  • Sugiyama, K., Hatano, K., & Yoshikawa, M. (2004). Adaptive web search based on user profile constructed without any effort from users. In Proceedings of the 13th international conference on world wide web (pp. 675–684). ACM.

  • Swaffar, J. K. (1988). Readers, texts, and second languages: The interactive processes. The Modern Language Journal, 72(2), 123–149.

    Article  Google Scholar 

  • Tseng, J. C., Chu, H. C., Hwang, G. J., & Tsai, C. C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education, 51(2), 776–786.

    Article  Google Scholar 

  • US Department of Education. (2017). Reimagining the role of technology in education: 2017 national education technology plan update (p. 9).

  • Vygotsky, L. (1978). Interaction between learning and development. Readings on the Development of Children, 23(3), 34–41.

    Google Scholar 

  • Wang, H. C., Li, T. Y., & Chang, C. Y. (2004). Adaptive presentation for effective web-based learning of 3D content. In Proceedings of IEEE international conference on advanced learning technologies (pp. 136–140). IEEE.

  • Wang, F. L., Zou, D., & Xie, H. (2018). Personalized word learning for ESL students via integration of implicit and explicit profiles. In International conference on blended learning (pp. 301–310). Springer.

  • Wang, H.-Y., Liu, G.-Z., & Hwang, G.-J. (2017). Integrating socio-cultural contexts and location-based systems for ubiquitous language learning in museums: A state of the art review of 2009–2014. British Journal of Educational Technology, 48(2), 653–671.

    Article  Google Scholar 

  • Wixted, J. T., & Ebbesen, E. B. (1997). Genuine power curves in forgetting: A quantitative analysis of individual subject forgetting functions. Memory & cognition, 25(5), 731–739.

    Article  Google Scholar 

  • Wu, D., Im, I., Tremaine, M., Instone, K., & Turoff, M. (2003). A framework for classifying personalization scheme used on e-commerce websites. In Proceedings of the 36th annual Hawaii international conference on system sciences (p. 12). IEEE.

  • Xie, H., Li, Q., Mao, X., Li, X., Cai, Y., & Rao, Y. (2014). Community-aware user profile enrichment in folksonomy. Neural Networks, 58, 111–121.

    Article  Google Scholar 

  • Xie, H., Zou, D., Lau, R. Y. K., Wang, F. L., & Wong, T.-L. (2016). Generating incidental word-learning tasks via topic-based and load-based profiles. IEEE Multimedia, 23(1), 60–70.

    Article  Google Scholar 

  • Zou, D., Xie, H., Wang, F. L., Wong, T.-L., Poon, C. K., & Ho, W.-S. (2015). Comparative study on heterogeneous profiling sources for second language learners. In International conference on technology in education (pp. 209–218). Springer.

  • Zou, D., Xie, H., Wong, T.-L., Wang, F. L., Kwan, R., & Chan, W. H. (2017a). An explicit learner profiling model for personalized word learning recommendation. In International symposium on emerging technologies for education (pp. 495–499). Springer.

  • Zou, D. (2017). Vocabulary acquisition through cloze exercises, sentence-writing and composition-writing: Extending the evaluation component of the involvement load hypothesis. Language Teaching Research, 21(1), 54–75.

    Article  Google Scholar 

  • Zou, D., & Xie, H. (2018). Personalized word-learning based on technique feature analysis and learning analytics. Journal of Educational Technology & Society, 21(2), 233–244.

    Google Scholar 

  • Zou, D., Xie, H., Rao, Y., Wong, T.-L., Wang, F. L., & Wu, Q. (2017). A comparative study on various vocabulary knowledge scales for predicting vocabulary pre-knowledge. International Journal of Distance Education Technologies (IJDET), 15(1), 69–81.

    Article  Google Scholar 

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Acknowledgements

The work described in this article was fully supported by the Standing Committee on Language Education and Research (EDB(LE)/P&R/EL/175/2), the Innovation and Technology Fund (Project No. GHP/022/17GD) of the Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region, and Eastern Scholar Chair Professorship Fund (No. JZ2017005) from Shanghai Municipal Education Commission of China. A preliminary study was published in the International Conference on Blended Learning 2018 (Wang et al. 2018), and this article has been thoroughly re-written after we studied new research questions, conducted extensive experiments, obtained new findings and found new implications. Di Zou is the corresponding author of this article.

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Authors and Affiliations

  1. Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China

    Haoran Xie

  2. Department of English Language Education, The Education University of Hong Kong, Hong Kong, China

    Di Zou & Ruofei Zhang

  3. Faculty of Education, The University of Hong Kong, Hong Kong, China

    Minhong Wang

  4. Department of Educational Information Technology, East China Normal University, Shanghai, China

    Minhong Wang

  5. The Open University of Hong Kong, Hong Kong, China

    Reggie Kwan

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  1. Haoran Xie
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Appendix

Appendix

See Table 2.

Table 2 Folse’s modified vocabulary knowledge scale (2006) was applied to measure the participants’ word learning outcomes. The target words were adapted from Zou’s research (2017)
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Xie, H., Zou, D., Zhang, R. et al. Personalized word learning for university students: a profile-based method for e-learning systems. J Comput High Educ 31, 273–289 (2019). https://doi.org/10.1007/s12528-019-09215-0

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