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RemedialTutor: A blended learning platform for weak students and study its efficiency in social science learning of middle school students in India

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Abstract

Blended learning is one of the leading trends in education. Blended learning combines computer-assisted learning with traditional classroom learning. The literature shows that the blended learning often helps the students to achieve better learning outcome. However, a majority of the existing learning platforms do not focus on the problems of weak students. Here our objective is to develop a computer-assisted learning platform that focuses on performance improvement of weak students and study the efficacy of the system. This paper presents the proposed system, RemedialTutor, that assists the weak students in effective preparation for an examination. The learning platform performs several tasks on demand; for example, providing the meaning of unknown words, sentence simplification, identification of questionable sentences, extraction of summarized content on a specific topic, preparation of question paper and automatic evaluation, identification of less confident sections, etc. To study the effectiveness of the proposed system, it is tested using a blended learning framework. The system is provided to the students as a supplement to the traditional classroom activities and resources. During the comparative study, the experiment group students used this system during their exam preparation but the control group students relied only on their regular resources. It is found that the experiment group students perform better than the control group. The t-value is 2.3466 and p-value is 0.0243. These values indicate that the difference is statistically significant.

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Notes

  1. https://stanfordnlp.github.io/CoreNLP/

  2. https://en.wikipedia.org/wiki/Cosine_similarity

References

  • Ahmad, A.-H. (2010). Constructivism based blended learning in higher education. Master Thesis, Master of Management Information Systems. Universiteit Hasselt.

  • Banerjee, A., Shawn, C., Esther, D. (2007). Lindenleigh remedying education: evidence from randomized experiments in India. The Quarterly Journal of Economics, 122(3), 1235–1264.

    Article  Google Scholar 

  • Bayraktar, S. (2000). A meta-analysis of the effectiveness of computer-assisted instruction in science education. Doctoral Dissertation, Athens: Ohio University. ISBN:0-599-86473-7.

    Google Scholar 

  • Bersin and Associates. (2003). Blended Learning: What Works? http://www.e-learningguru.com/wpapers/blended_bersin.doc.

  • Blei, D.M., Ng, A.Y., Jordan, M.I., Lafferty, J. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research.

  • Cheung, A.C., & Slavin, R.E. (2012). How features of educational technology applications affect student reading outcomes: a meta-analysis. Educational Research Review, 7(3), 198–215. https://doi.org/10.1016/j.edurev.2012.05.002.

    Article  Google Scholar 

  • Christmann, E.P., & Badgett, J.L. (2000). The comparative effectiveness of CAI on collegiate academic performance. Journal of Computing in Higher Education, 11(2), 91–103.

    Article  Google Scholar 

  • Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P. (2011). Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research (JMLR).

  • Conneau, A., Douwe, K., Holger, S., Loic, B., Bordes, A. (2017). Supervised learning of universal sentence representations from natural language inference data. Volume 1. arXiv:1705.02364.

  • Dean, P., Stahl, M., Sylwester, D., Pear, J. (2001). Effectiveness of combined delivery modalities for distance learning and resident learning. Quarterly Review of Distance Education, 2(3), 247–254.

    Google Scholar 

  • DeLacey, B.J., & Leonard, D.A. (2002). Case study on technology and distance in education at the Harvard business school. Educational Technology and Society, 5 (2), 13–28.

    Google Scholar 

  • Dziuban, C., Hartman, J., Moskal, P. (2004). Blended learning. Research bulletin, university of central Florida. Educause Center for Applied Research, 2004 (7), 1–12.

    Google Scholar 

  • Gosset, W.S. (1908). The probable error of a mean. Biometrika, 6(1), 1–25.

    Article  MathSciNet  Google Scholar 

  • Graham, C. (2006). Blended learning systems: definition, current trends, and future directions. In Handbook of blended learning: global perspectives, local designs (p. 2006). San Francisco: Pfeiffer Publishing.

  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  Google Scholar 

  • Joy, M., Muzykantskii, B., Rawles, S., Evans, M. (2002). An infrastructure for web-based computer-assisted learning. ACM Journal of Educational Resources, 2 (4), 1–19.

    Google Scholar 

  • Karaksha, A., Grant, G., Davey, A., Dukie, S. A. (2011). Development and evaluation of computer-assisted learning (CAL) teaching tools compared to the conventional didactic lecture in pharmacology education. In Proceedings of Edulearn11- international conference on education and new learning technologies (p. 3580–3589).

  • Kim, W. (2007). Towards a definition and methodology for blended learning. In Proceedings of the workshop on blended learning 2007 (p. 1–8). Edinburgh, United Kingdom.

  • Koedinger, K.R., McLaughlin, E.A., Heffernan, N.T. (2010). A quasi-experimental evaluation of an on-line formative assessment and tutoring system. Journal of Educational Computing Research, 43(4), 489–510.

    Article  Google Scholar 

  • Kulik, J.A. (1994). Meta-analytic studies of findings on computer-based instruction. In Baker, E.L., & O’Neill, H.F. (Eds.) Technology assessment in education and training (pp. 9–33). Hillsdale: Lawrence Erlbaum.

  • Lin, Y.T., Tseng, S.S., Tsai, C.J. (2002). The design and implementation of a computer-assisted learning expert system. International Journal of Computer Processing of Languages, 15(1), 33–61.

    Article  Google Scholar 

  • Majumdar, M., & Saha, S.K. (2014). Automatic selection of informative sentences: the sentences that can generate multiple choice questions. Knowledge Management and E-Learning: An International Journal., 6(4), 377–391.

    Google Scholar 

  • Marneffe, M.C.D., & Manning, C.D. (2008). Stanford typed dependencies manual (pp. 338–345). Technical Report, Stanford University. https://nlp.stanford.edu/software/dependencies_manual.pdf.

  • Mihalcea, R., & Tarau, P. (2004). TextRank: bringing order into texts. In Proceedings the conference on empirical methods in natural language processing (EMNLP-04).

  • Miller, G.A., Beckwith, R., Fellbaum, C.D., Gross, D., Miller, K. (1990). Wordnet: an online lexical database. International Journal of Lexicographics, 3(4), 235–244.

    Article  Google Scholar 

  • O’Connor, B., & Heilman, M. (2013). ARKRef: a rule-based coreference resolution system. arXiv:1310.1975.

  • Pilli, O., & Aksu, M. (2013). The effects of computer-assisted instruction on the achievement, attitudes and retention of fourth grade mathematics students in North Cyprus. Computers and Education, 62, 62–71.

    Article  Google Scholar 

  • Ray, P.P. (2010). Web based e-learning in India: the cumulative views of different aspects. Indian Journal of Computer Science and Engineering, 1(4), 340–352.

    Google Scholar 

  • Rayne, R., & Baggott, G. (2004). Computer-based and computer assisted tests to assess procedural and conceptual knowledge. In Proceedings of the 8th CAA conference. Loughborough: Loughborough University.

  • Reimer, K., & Mayer, P.S. (2005). Third-graders learn about fractions using virtual manipulatives: a classroom study. The Journal of Computers in Mathematics and Science Teaching, 24(1), 5–25.

    Google Scholar 

  • Sharma, R. (2017). Computer assisted learning – a study. International Journal of Advanced Research in Education and Technology (IJARET), 4(2), 102–105.

    Google Scholar 

  • Shashi Rekha, M. (2013). Blended learning in India: are teachers in India ready to go blended? Technical report. India: Jain University.

    Google Scholar 

  • Roschelle, J., Shechtman, N., Tatar, D., Hegedus, S., Hopkins, B., Empson, S., Knudsen, J., Gallagher, L. (2010). Integration of technology, curriculum, and professional development for advancing middle school mathematics: three large-scale studies. American Educational Research Journal, 47(4), 833–878.

    Article  Google Scholar 

  • Sharma, D., & Malhotra, P. (2016). A comparison of computer assisted learning and practical animal experiment for undergraduate medical students in pharmacology curriculum - a questionnaire based study conducted in a medical college of North India. International Journal of Basic and Clinical Pharmacology, 5(6), 2581–2584.

    Article  Google Scholar 

  • Wang, Y., & Liao, H.C. (2017). Learning performance enhancement using computer-assisted language learning by collaborative learning groups. Symmetry, 2017 (9), 141. https://doi.org/10.3390/sym9080141.

    Article  Google Scholar 

  • Yapici, I., & Akbayin, H. (2012). Umit the effect of blended learning model on high school students. Biology Achievement and on Their Attitudes Towards the Internet the Turkish Online Journal of Educational Technology, 11(2), 228–237.

    Google Scholar 

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Funding

This work is partially supported by the sponsored research project grant (project file no.: YSS/ 2015/ 001948) provided by the Science and Engineering Research Board (SERB), Govt. of India.

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  1. Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, 835215, India

    Dhawaleswar Rao CH & Sujan Kumar Saha

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  1. Dhawaleswar Rao CH
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  2. Sujan Kumar Saha
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Correspondence to Sujan Kumar Saha.

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CH, D.R., Saha, S.K. RemedialTutor: A blended learning platform for weak students and study its efficiency in social science learning of middle school students in India. Educ Inf Technol 24, 1925–1941 (2019). https://doi.org/10.1007/s10639-018-9813-4

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