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An Adaptive and Personalized English Reading Recommendation System

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Learning, Design, and Technology

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Abstract

Adaptive and personalized English learning systems are rapidly growing in popularity, providing learning content, and satisfying the demands of learners according to various learning conditions and situations. Appropriate contents can influence learning motivation and even affect learning achievement. Moreover, adaptive learning guiding not only can help learners to promote English skills about vocabulary, sentence, and context comprehension but also effectively reduce the anxiety of learning English and enhance interest in learning English, especially for English as foreign language learners. Therefore, this study developed an adaptive and personalized English reading recommendation learning system. In addition to providing general auxiliary functions (such as vocabulary search, text and figure annotation, and highlighting), this system uses learner ability, article difficulty, and article relevance analyzed from learning portfolios as the computing parameters for recommending suitable articles according to learner needs during the reading process. The proposed system can achieve adaptive and personalized learning.

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References

  • Abdel-Ghaffar, S., Gallant, J., Huth, A., Stansbury, D., Cowen, A., & Bishop, S. (2015). Using voxel-wise encoding models to study occipito-temporal representations of the animacy, semantic and affective content of natural images. Journal of Vision, 15(12), 508.

    Article  Google Scholar 

  • AbuSeileek, A. F. (2012). The effect of computer-assisted cooperative learning methods and group size on the EFL learners’ achievement in communication skills. Computers & Education, 58(1), 231–239.

    Article  Google Scholar 

  • Adam, M., & Vogel, R. (2014). Comparative Czech-English analysis of written English (KAPA): Transforming linguistic expertise into a practical EAP course.

    Google Scholar 

  • Almekhlafi, A. G. (2006). The effect of computer assisted language learning (CALL) on United Arab Emirates English as a foreign language (EFL) school students’ achievement and attitude. Journal of Interactive Learning Research, 17(2), 121–142.

    Google Scholar 

  • Amin, N., & Rahman, N. A. (2014). A new revolution in Malays’ names: The association of identity identification with English language learning perception. Paper presented at the Recent Trends in Social and Behaviour Sciences: Proceedings of the International Congress on Interdisciplinary Behaviour and Social Sciences 2013. Jakarta, Indonesia.

    Google Scholar 

  • Arapakis, I., Jose, J. M., & Gray, P. D. (2008). Affective feedback: An investigation into the role of emotions in the information seeking process. Paper presented at the Proceedings of the 31st annual international ACM SIGIR conference on Research and Development in Information Retrieval. New York, USA.

    Google Scholar 

  • Carreira, J. M., Ozaki, K., & Maeda, T. (2013). Motivational model of English learning among elementary school students in Japan. System, 41(3), 706–719.

    Article  Google Scholar 

  • Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. In Evolution of teaching and learning paradigms in intelligent environment (pp. 183–221). Berlin, NY: Springer.

    Chapter  Google Scholar 

  • Chen, G.-D., Chang, C.-K., & Wang, C.-Y. (2008). Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques. Computers & Education, 50(1), 77–90.

    Article  Google Scholar 

  • Cheung, A. C., & Slavin, R. E. (2012a). The effectiveness of educational technology applications for enhancing reading achievement for K-12 classrooms: A meta-analysis. Best Evidence Encyclopedia.

    Google Scholar 

  • Cheung, A. C., & Slavin, R. E. (2012b). How features of educational technology applications affect student reading outcomes: A meta-analysis. Educational Research Review, 7(3), 198–215.

    Google Scholar 

  • Crooks, T. J. (1988). The impact of classroom evaluation practices on students. Review of Educational Research, 58(4), 438–481.

    Article  Google Scholar 

  • Davis, J. D., Winkielman, P., & Coulson, S. (2015). Facial action and emotional language: ERP evidence that blocking facial feedback selectively impairs sentence comprehension. Journal of Cognitive Neuroscience, 27(11), 2269–2280.

    Article  Google Scholar 

  • Duijnhouwer, H., Prins, F. J., & Stokking, K. M. (2010). Progress feedback effects on students’ writing mastery goal, self-efficacy beliefs, and performance. Educational Research and Evaluation, 16(1), 53–74.

    Article  Google Scholar 

  • Erben, T., Ban, R., & Castañeda, M. (2008). Teaching English language learners through technology. New York, NY: Routledge.

    Google Scholar 

  • Feidakis, M., Caballé, S., Daradoumis, T., Jiménez, D. G., & Conesa, J. (2014). Providing emotion awareness and affective feedback to virtualised collaborative learning scenarios. International Journal of Continuing Engineering Education and Life Long Learning, 24(2), 141–167.

    Article  Google Scholar 

  • Ferris, D. R. (1997). The influence of teacher commentary on student revision. Tesol Quarterly, 31(2), 315–339.

    Article  Google Scholar 

  • Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221.

    Article  Google Scholar 

  • Frechette, C., & Moreno, R. (2010). The roles of animated pedagogical agents’ presence and nonverbal communication in multimedia learning environments. Journal of Media Psychology, 22(2), 61–72.

    Google Scholar 

  • Fu, X. (2014). An action research report on promoting learners’ motivation in college English intensive reading course in China. Theory and Practice in Language Studies, 4(4), 715–724.

    Article  Google Scholar 

  • Fuller, M., Healey, M., Bradley, A., & Hall, T. (2004). Barriers to learning: A systematic study of the experience of disabled students in one university. Studies in Higher Education, 29(3), 303–318.

    Article  Google Scholar 

  • Gibson, C., & Gibb, F. (2011). An evaluation of second-generation ebook readers. The Electronic Library, 29(3), 303–319.

    Article  Google Scholar 

  • Glance, N. S. (2005). Recommender system and method for generating implicit ratings based on user interactions with handheld devices: Google Patents.

    Google Scholar 

  • Hartshorne, R., & Okraski, H. (2015). Developing a Modeling & Simulation Curriculum for High School: Processes and Lessons Learning. Paper presented at the Society for Information Technology & Teacher Education International Conference. Chesapeake, VA, USA.

    Google Scholar 

  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.

    Article  Google Scholar 

  • Heszen-Niejodek, I., Gottschalk, L. A., & Januszek, M. (1999). Anxiety and hope during the course of three different medical illnesses: A longitudinal study. Psychotherapy and Psychosomatics, 68(6), 304–312.

    Article  Google Scholar 

  • Hidi, S., & Anderson, V. (1986). Producing written summaries: Task demands, cognitive operations, and implications for instruction. Review of Educational Research, 56(4), 473–493.

    Article  Google Scholar 

  • Honigsfeld, A., & Dove, M. G. (2012). Coteaching and other collaborative practices in the EFL/ESL classroom: Rationale, research, reflections, and recommendations. Charlotte, NC: IAP.

    Google Scholar 

  • Hsieh, T.-C., Wang, T.-I., Su, C.-Y., & Lee, M.-C. (2012). A fuzzy logic-based personalized learning system for supporting adaptive English learning. Journal of Educational Technology & Society, 15(1), 273–288.

    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, S.-C. (2014). The effect of using an instructional e-book on junior high school students’ English learning achievement.

    Google Scholar 

  • Huang, Y. M., & Liang, T. H. (2015). A technique for tracking the reading rate to identify the e-book reading behaviors and comprehension outcomes of elementary school students. British Journal of Educational Technology, 46(4), 864–876.

    Article  Google Scholar 

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

    Google Scholar 

  • Huang, Y.-M., Liang, T.-H., Su, Y.-N., & Chen, N.-S. (2012b). Empowering personalized learning with an interactive e-book learning system for elementary school students. Educational Technology Research and Development, 60(4), 703–722.

    Google Scholar 

  • Huang, Y.-M., Liao, Y.-W., Huang, S.-H., & Chen, H.-C. (2014). Jigsaw-based cooperative learning approach to improve learning outcomes for mobile situated learning. Journal of Educational Technology & Society, 17(1), 128–140.

    Google Scholar 

  • Huang, S.-H., Huang, Y.-M., Wu, T.-T., Chen, H.-R., & Chang, S.-M. (2015). Problem-based learning effectiveness on micro-blog and blog for students: A case study. Interactive Learning Environments . Advance online publication. doi:10.1080/10494820.2015.1004353

    Google Scholar 

  • Huba, M. E., & Freed, J. E. (2000). Learner centered assessment on college campuses: Shifting the focus from teaching to learning. Community College Journal of Research and Practice, 24(9), 759–766.

    Article  Google Scholar 

  • Hwang, G.-J., & Wu, P.-H. (2014). Applications, impacts and trends of mobile technology-enhanced learning: A review of 2008–2012 publications in selected SSCI journals. International Journal of Mobile Learning and Organisation, 8(2), 83–95.

    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 

  • KlaÅ¡nja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885–899.

    Article  Google Scholar 

  • Korat, O., & Shamir, A. (2008). The educational electronic book as a tool for supporting children’s emergent literacy in low versus middle SES groups. Computers & Education, 50(1), 110–124.

    Article  Google Scholar 

  • Kuo, Y.-C., Chu, H.-C., & Huang, C.-H. (2015). A learning style-based grouping collaborative learning approach to improve EFL students’ performance in English courses. Educational Technology & Society, 18(2), 284–298.

    Google Scholar 

  • Kuşçu, S., & Ãœnlü, S. (2015). Teaching translation: A suggested lesson plan on translation of advertising through the use of authentic materials. Procedia-Social and Behavioral Sciences, 199, 407–414.

    Article  Google Scholar 

  • Lee, K.-H., Guttenberg, N., & McCrary, V. (2002). Standardization aspects of eBook content formats. Computer Standards & Interfaces, 24(3), 227–239.

    Article  Google Scholar 

  • Li, L.-Y. (2015). Development and evaluation of a Web-based e-book with a concept mapping system. Journal of Computers in Education, 2(2), 211–226.

    Article  Google Scholar 

  • Liang, T.-H., & Huang, Y.-M. (2014). An investigation of reading rate patterns and retrieval outcomes of elementary school students with e-books. Journal of Educational Technology & Society, 17(1), 218–230.

    Google Scholar 

  • Liou, W.-S. (2015). Effects of an interactive e-book approach on students’ achievement, motivation and cognitive load in learning analects of confucians.

    Google Scholar 

  • Liou, H.-C., & Li, S.-Y. (2014). The effects of computerized graphic organizers on students’ performance in English reading and writing tasks. International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 4(3), 1–19.

    Article  Google Scholar 

  • Ma, Y.-H., & Lin, W.-Y. (2015). A study on the relationship between English reading comprehension and English vocabulary knowledge. Education Research International. Advance online publication. doi:10.1155/2015/209154.

    Google Scholar 

  • Mooney, R. J., & Roy, L. (2000). Content-based book recommending using learning for text categorization. Paper presented at the Proceedings of the fifth ACM conference on Digital Libraries. New York, USA.

    Google Scholar 

  • Nelson, M. M., & Schunn, C. D. (2009). The nature of feedback: How different types of peer feedback affect writing performance. Instructional Science, 37(4), 375–401.

    Article  Google Scholar 

  • Pal, N., & Rangaswamy, A. (2003). The power of one: Gaining business value from personalization technologies. Victoria, BC: Trafford Publishing.

    Google Scholar 

  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.

    Article  Google Scholar 

  • Park, E., Sung, J., & Cho, K. (2015a). Reading experiences influencing the acceptance of e-book devices. The Electronic Library, 33(1), 120–135.

    Article  Google Scholar 

  • Park, E., Sung, J., & Cho, K. (2015b). Reading experiences influencing the acceptance of e-book devices. The Electronic Library, 33(1), 120–135.

    Article  Google Scholar 

  • Quade, A. M. (1996). An Assessment of Retention and Depth of Processing Associated with Notetaking Using Traditional Pencil and Paper and an On-Line Notepad During Computer-Delivered Instruction. Indianapolis, IN, USA.

    Google Scholar 

  • Rabbi, M., Aung, M. H., Zhang, M., & Choudhury, T. (2015). MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones. Paper presented at the Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, USA.

    Google Scholar 

  • Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. New York, NY: Springer.

    Book  Google Scholar 

  • Salvia, J., Ysseldyke, J., & Bolt, S. (2012). Assessment: In special and inclusive education. Boston, MA: Cengage Learning.

    Google Scholar 

  • Sawyer, B. E., Justice, L. M., Guo, Y., Logan, J. A., Petrill, S. A., Glenn-Applegate, K.,… Pentimonti, J. M. (2014). Relations among home literacy environment, child characteristics and print knowledge for preschool children with language impairment. Journal of Research in Reading 37(1), 65–83.

    Google Scholar 

  • Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2008). Motivation in education: theory, research, and applications. NJ, USA: Pearson/Merrill Prentice Hall.

    Google Scholar 

  • Shamir, A., & Shlafer, I. (2011). E-books effectiveness in promoting phonological awareness and concept about print: A comparison between children at risk for learning disabilities and typically developing kindergarteners. Computers & Education, 57(3), 1989–1997.

    Article  Google Scholar 

  • Shiratuddin, N., Landoni, M., Gibb, F., & Hassan, S. (2006). E-Book technology and its potential applications in distance education. Journal of Digital Information, 3(4). Retrieved from https://journals.tdl.org/jodi/index.php/jodi/article/view/90

  • Singh, K. V., & Tripathi, A. K. (2012). Contextual query perfection by affective features based implicit contextual semantic relevance feedback in multimedia information retrieval. IJCSI International Journal of Computer Science Issues, 9(5), 191–202.

    Google Scholar 

  • Sohn, W.-S., Ko, S.-K., Lee, K.-H., Kim, S.-H., Lim, S.-B., & Choy, Y.-C. (2002). Standardization of eBook documents in the Korean industry. Computer Standards & Interfaces, 24(1), 45–60.

    Article  Google Scholar 

  • Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information Management, 33(1), 1–11.

    Article  Google Scholar 

  • Thiel, C., & Schwarting, R. (2001). Dopaminergic lateralisation in the forebrain: Relations to behavioural asymmetries and anxiety in male Wistar rats. Neuropsychobiology, 43(3), 192–199.

    Article  Google Scholar 

  • Timmers, C., & Veldkamp, B. (2011). Attention paid to feedback provided by a computer-based assessment for learning on information literacy. Computers & Education, 56(3), 923–930.

    Article  Google Scholar 

  • Topolinski, S., & Deutsch, R. (2013). Phasic affective modulation of semantic priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(2), 414.

    Google Scholar 

  • Tortorella, R. A., & Graf, S. (2015). Considering learning styles and context-awareness for mobile adaptive learning. Education and Information Technologies. Advance online publication. doi:10.1007/s10639-015-9445-x

    Google Scholar 

  • Tseng, C.-H., & Tsai, H.-J. (2007). Molecular characterization of a new serotype of duck hepatitis virus. Virus Research, 126(1), 19–31.

    Article  Google Scholar 

  • Wang, S.-L., & Wu, C.-Y. (2011). Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications, 38(9), 10831–10838.

    Article  Google Scholar 

  • Wei, F. H., & Chen, G. D. (2006). Collaborative mentor support in a learning context using a ubiquitous discussion forum to facilitate knowledge sharing for lifelong learning. British Journal of Educational Technology, 37(6), 917–935.

    Article  Google Scholar 

  • Wilson, R. (2003). Ebook readers in higher education. Journal of Educational Technology & Society, 6(4), 8–17.

    Google Scholar 

  • Woody, W. D., Daniel, D. B., & Baker, C. A. (2010). E-books or textbooks: Students prefer textbooks. Computers & Education, 55(3), 945–948.

    Article  Google Scholar 

  • Wu, P.-R. (2013). The using intention and effectiveness.

    Google Scholar 

  • Wu, T.-T. (2015). A learning log analysis of an English-reading e-book system combined with a guidance mechanism. Interactive Learning Environments. Advance online publication. doi:10.1080/10494820.2015.1070272

    Google Scholar 

  • Wu, T.-T., Huang, Y.-M., Chao, H.-C., & Park, J. H. (2014). Personalized English reading sequencing based on learning portfolio analysis. Information Sciences, 257, 248–263.

    Article  Google Scholar 

  • Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2013). Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Journal of Educational Technology & Society, 16(4), 185–200.

    Google Scholar 

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Acknowledgments

This study was supported by the Ministry of Science and Technology (MOST) under Grant MOST 103-2511-S-224 -004 -MY3 and MOST 104-2511-S-224 -003 -MY3.

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Authors and Affiliations

  1. Graduate School of Technological and Vocational Education, National Yunlin University of Science and Technology, 123 University Road, Section 3, 64002, Douliou, Yunlin, Taiwan

    Ting-Ting Wu

  2. Department of Information and Learning Technology, National University of Tainan, No. 33, Sec. 2, Shu-Lin Street, 70005, Tainan, Taiwan

    Shu-Hsien Huang

Authors
  1. Ting-Ting Wu
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  2. Shu-Hsien Huang
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Correspondence to Ting-Ting Wu or Shu-Hsien Huang .

Editor information

Editors and Affiliations

  1. College of Info, Ste G150, Univ of North Texas, Denton, Texas, USA

    Michael J Spector

  2. School of Education, Virginia Tech, Blacksburg, Virginia, USA

    Barbara B Lockee

  3. School of Education, Baker University, Overland Park, Kansas, USA

    Marcus D. Childress

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Wu, TT., Huang, SH. (2016). An Adaptive and Personalized English Reading Recommendation System. In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_29-1

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  • DOI: https://doi.org/10.1007/978-3-319-17727-4_29-1

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  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-17727-4

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