Abstract
There is a quest to provide education from anywhere, at any time and for anyone, using digital information and communication technologies. However, there is no equivalent increase in support for the instructors responsible for maintaining such courses, which is evidenced by the large number of dropouts and failures in such courses, to which learners justify as a lack of support from instructors. Instructors, however, complaint about the huge effort to manage such courses. In order to provide this support, instructors would have to: (1) to discover situations of pedagogical interest occurring in their courses; (2) understand these situations; (3) make decisions to address them; (4) monitor and evaluate the impact of the decision made. However, instructors do not master these abilities, nor is it practical or appropriate to ask them to do so. We propose a process, and an authoring solution that implements it, to guide pedagogical decision-making in online learning environments. Our proposal is based on decision-making informed by educational data and data visualization, with the assistance of an authoring system to promote cooperation between artificial intelligence and human intelligence. We conducted experiments, using a MOOC named MeuTutor-ENEM, to evaluate the process and the authoring solution, and the results indicate that the process helped instructors make better pedagogical decisions, and the authoring solution was positively perceived by the instructors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
MOOCs: Massive Open Online Courses.
- 2.
- 3.
Due to limitations in the number of pages for this chapter, we had to focus on the essentials. This way, the description of the proposal, the description of the experiments and the results were summarized to their core.
- 4.
More information about the PDMP can be found in [22].
- 5.
Teachersā Partner.
- 6.
Educational resources are resources for teaching and learning, including complete courses, teaching material, modules, textbooks, videos, quizzes, educational software and any other tools or techniques used to support access to knowledge [12].
- 7.
The percentage of students that followed the instructorsā recommendation.
- 8.
At least 26 learners followed the recommendation.
- 9.
Named in the learning environment as āMissionsā.
- 10.
The learners avoiding problem solving were recommended to do so, that is why we saw an increase in problem solving (both correctly and incorrectly), which means learners were doing what they were asked to.
- 11.
There was a small difference in step 4, regarding the utility perception and the intention to use.
- 12.
We used the colors Red, Amber and Green to categorize learners according to their performance/pedagogical situation (inadequate: red; insufficient: amber/yellow and adequate: green).
- 13.
Inadequate, insufficient and adequate.
References
Baker, R.S.: Stupid tutoring systems, intelligent humans. Int. J. Artif. Intell. Educ. 26(2), 600ā614 (2016)
Belanger, Y., Thornton, J.: Bioelectricity: A Quantitative Approach, Duke Universityās First MOOC (2013)
Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief, US Department of Education, Office of Educational Technology, pp. 1ā57 (2012)
Bittencourt, I.I., Costa, E., Silva, M., Soares, E.: A computational model for developing semantic web-based educational systems. Knowl.-Based Syst. 22(4), 302ā315 (2009)
Cabada, R.Z., Estrada, M.L.B., GarcĆa, C.A.R.: Educa: a web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network. Expert Syst. Appl. 38(8), 9522ā9529 (2011)
Chou, C.-Y., Huang, B.-H., Lin, C.-J.: Complementary machine intelligence and human intelligence in virtual teaching assistant for tutoring program tracing. Comput. Educ. 57(4), 2303ā2312 (2011)
Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40(11), 4715ā4729 (2013)
De Bra, P., et al.: ALAT: finally an easy to use adaptation authoring tool. In: Proceedings of the 27th ACM Conference on Hypertext and Social Media, pp. 213ā218. ACM (2016)
AssociaĆ§Ć£o Brasileira de EducaĆ§Ć£o Ć” DistĆ¢ncia. Censo ead br (2015). RelatĆ³rio AnalĆtico da Aprendizagem a DistĆ¢ncia no Brasil, 2016
Dermeval, D., Paiva, R., Bittencourt, I.I., Vassileva, J., Borges, D.: Authoring tools for designing intelligent tutoring systems: a systematic review of the literature. Int. J. Artif. Intell. Educ. 1ā49
Foss, J.G.K., Cristea, A.I.: The next generation authoring adaptive hypermedia: using and evaluating the MOT3. 0 and PEAL tools. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, pp. 83ā92. ACM (2010)
The William Flora Hewlett Foundation: White paper: Open educational resources. breaking the lockbox on education. Technical report, The William Flora Hewlett Foundation (2013)
Heffernan, N.T., Heffernan, C.L.: The assistments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. Int. J. Artif. Intell. Educ. 24(4), 470ā497 (2014)
Jain, A.K.: Data Clustering: 50 Years Beyond K-means. Pattern Recogn. Lett. 31(8), 651ā666 (2010)
Kopcha, T.J.: Teachersā perceptions of the barriers to technology integration and practices with technology under situated professional development. Comput. Educ. 59(4), 1109ā1121 (2012)
Liyanagunawardena, T.R., Parslow, P., Williams, S.: Dropout: MOOc participantsā perspective (2014)
Murray, T.: Authoring intelligent tutoring systems: an analysis of the state of the art. Int. J. Artif. Intell. Educ. (IJAIED) 10, 98ā129 (1999)
Murray, T.: An overview of intelligent tutoring system authoring tools: updated analysis of the state of the art. In: Murray, T., Blessing, S.B., Ainsworth, S. (eds.) Authoring Tools for Advanced Technology Learning Environments, pp. 491ā544. Springer, Heidelberg (2003). https://doi.org/10.1007/978-94-017-0819-7_17
Onah, D.F.O., Sinclair, J., Boyatt, R.: Dropout rates of massive open online courses: behavioural patterns. In: EDULEARN14 Proceedings, pp. 5825ā5834 (2014)
Paiva, R., Barbosa, A., Batista, E., Pimentel, D., Bittencourt, I.I.: Badges and XP: an observational study about learning. In: Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE, pp. 1ā8. IEEE (2015)
Paiva, R., Bittencourt, I.I., da Silva, A.P.: Uma ferramenta para recomendaĆ§Ć£o pedagĆ³gica baseada em mineraĆ§Ć£o de dados educacionais. In: Anais dos Workshops do Congresso Brasileiro de InformĆ”tica na EducaĆ§Ć£o, vol. 2 (2013)
Paiva, R., Bittencourt, I.I., TenĆ³rio, T., Jaques, P., Isotani, S.: What do students do on-line? Modeling studentsā interactions to improve their learning experience. Comput. Hum. Behav. 64, 769ā781 (2016)
Paiva, R.O.A., Bittencourt, I.I., da Silva, A.P., Isotani, S., Jaques, P.: Improving pedagogical recommendations by classifying students according to their interactional behavior in a gamified learning environment. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 233ā238. ACM (2015)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40(6), 601ā618 (2010)
Romero, C., Ventura, S.: Educational data science in massive open online courses. Wiley Interdisciplinary Rev. Data Mining Knowl. Discov. (2016)
Schildkamp, K., Lai, M.K., Earl, L. (eds.): Data-Based Decision Making in Education: Challenges and Opportunities, vol. 17. Springer, Heidelberg (2012). https://doi.org/10.1007/978-94-007-4816-3
Siemens, G., d Baker, R.S.J.: Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 252ā254. ACM (2012)
Bittencourt, I.I., et al.: Evaluating the impact of Mars and Venus effect on the use of an adaptive learning technology for Portuguese and Mathematics. In: The 16th IEEE International Conference on Advanced Learning Technologies, ICALT 2016 (2016)
Sottilare, R., Graesser, A., Hu, X., Brawner, K.: Design Recommendations for Intelligent Tutoring Systems: Authoring Tools and Expert Modeling Techniques (2015). Ed by Robert Sottilare
Teo, T.: A path analysis of pre-service teachersā attitudes to computer use: applying and extending the technology acceptance model in an educational context. Interact. Learn. Environ. 18(1), 65ā79 (2010)
Teo, T.: Factors influencing teachersā intention to use technology: model development and test. Comput. Educ. 57(4), 2432ā2440 (2011)
Teo, T., Luan, W.S., Sing, C.C.: A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: an application of the technology acceptance model (TAM). Educ. Technol. Soc. 11(4), 265ā280 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Paiva, R., Bittencourt, I.I. (2018). Helping MOOC Teachers Do Their Job. In: Cristea, A., Bittencourt, I., Lima, F. (eds) Higher Education for All. From Challenges to Novel Technology-Enhanced Solutions. HEFA 2017. Communications in Computer and Information Science, vol 832. Springer, Cham. https://doi.org/10.1007/978-3-319-97934-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-97934-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97933-5
Online ISBN: 978-3-319-97934-2
eBook Packages: Computer ScienceComputer Science (R0)