Abstract
Conducting an early learner profile for a particular subject domain may help identify whether there is a potential market for the conceptualized open-shared learning objects and sequences…and may inform learning designs for target and potential learners. This chapter focuses on the importance of learner-centered design or the general idea that the design of learning accommodates understood learner needs and interests. This is not to say that all learner needs are accommodated because there are learning benefits for those who are able to adjust and adapt to the learning context. This chapter describes the importance of rough learner profiling as a framework, some relevant dimensions of such profiling, and how to use such profiles to enhance the design, development, and delivery of open-shared learning contents. This work shows the importance of using empirics to profile target and potential learners, and also to use profiles to constructive ends, not any potentially harmful ones (such as stereotyping and limiting learner options or denying access to particular groups). Also, this work emphasizes the efficacy-testing of learner profiling on learning resource designs and development and the resultant learning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abyaa A, Idrissi MK, and Bennani S. An adult learner’s knowledge model based on ontologies and rule reasoning. In SCAMS ’17. Oct. 25 – 27, 2017. Tangier, Morocco. Association for Computing Machinery. 1 – 6, 2017.
Ammari A, Lau L, and Dimitrova V. Deriving group profiles from social media to facilitate the design of simulated environments for learning. In LAK’12. Apr. 29 – May 2, 2012. Vancouver, BC, Canada. 198 – 207, 2012.
Atif Y. An architectural specification for a system to adapt to learning patterns. Educ Inf Technol 16: 259 – 279, 2011. https://doi.org/10.1007/s10639-010-9125-9.
Brusilovsky P. Developing adaptive educational hypermedia systems from design models to authoring tools. In Murray T., Blessing S.B., Ainsworth S. (eds). Authoring Tools for Advanced Technology Learning Environments. Springer, Dordrecht. 2003.
Chen G, Davis D, Lin J, Hauff C, and Houben G-J. Beyond the MOOC platform: Gaining insights about learners from the Social Web. Websci ’16. May 22 – 25, 2016. Hannover, Germany. 14 – 25, 2016. https://doi.org/10.1145/2908131.2908145.
Ҫimen OA. Mathematics learner profiling using behavioral, physiological and self-reporting methods. Thesis. Simon Fraser University. 2003.
Corrin L, de Barba PG, and Bakharia A. Using learning analytics to explore help-seeking learner profiles in MOOCs. In LAK ’17. Vancouver, B.C., Canada. https://doi.org/10.1145/3027385.3027448. 1 – 5, 2017.
Dagger D, O’Connor A, Lawless S, Walsh E, and Wade, VP. Service-oriented e-learning platforms: From monolithic systems to flexible services. IEEE Internet Computing. 28 – 35, 2007.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13: 319–340, 1989; https://doi.org/10.2307/249008.
De Freitas S, and Jarvis S. Towards a development approach to serious games. In T. Connolly, M. Stansfield, and L. Boyle’s Games-Based Learning Advancements for Multi-Sensory Human Computer Interfaces: Techniques and Effective Practices. Hershey: IGI Global. https://www.igi-global.com/chapter/towards-development-approach-serious-games/18797. 2009.
Dweck C. Carol Dweck revisits the ‘growth mindset.’ Education Week. 2015.
Dweck C. and Leggett EL. A social-cognitive approach to motivation and personality. Psychological Review 95: 256 – 273, 1988.
Eyssautier-Bayay C, Jean-Daubias S, and Pernin J-P. A model of learners profiles management process. In the Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling. IOS Press. Amsterdam, The Netherlands. 265 – 272, 2009.
Farina K, and Nitsche M. Outside the brick: Exploring prototyping for the elderly. In the proceedings of the British HCI 2015. July 13 – 17, 2015. Lincoln, United Kingdom. 11 – 17, 2015.
Garcia-Peñalvo FJ, Hermo VF, Blanco AF, and Sein-Echaluce M. Applied educational innovation MOOC: Learners’ experience and valorization of strengths and weaknesses. In TEEM ’14. Oct. 1 – 3, 2014. Salamanca, Spain. 2014. https://doi.org/10.1145/2669711.2669892.
Germanakos P, Tsianos N, Lekkas Z, Mourlas C, and Samaras G. Capturing essential intrinsic user behavior values for the design of comprehensive web-based personalized environments. Computers in Human Behavior 24: 1434 – 1451, 2008.
Guerra J. Open social learner models for self-regulated learning and learning motivation. In UMAP ’16. July 13 – 17, 2016. Halifax, NS, Canada. 329 – 332, 2016; https://doi.org/10.1145/2930238.2930375.
Hai-Jew S. Designing online earning to actual human capabilities. In the College and University Professional Association for Human Resources (CUPA-HR) Midwest Regional Conference 2016. https://www.slideshare.net/ShalinHaiJew/designing-online-learning-to-actual-human-capabilities. 2016.
Harrak F, Bouchet F, Luengo V, and Gillois P. PHS profiling students from their questions in a blended learning environment. In LAK’18. Mar. 7 – 9, 2018. Sydney, NSW, Australia. 102 – 110, 2018. https://doi.org/10.1145/3170358.3170389.
Heng LE, Sangodiah A, Muniandy M, and Yuen PK. Integration of learner’s model into learning management system environment. Journal of Fundamental and Applied Sciences 10: 1771-1778, 2018. https://doi.org/10.4314/jfas.v10i6s.141.
Jraidi I, Chaouachi M, and Frasson C. A dynamic multimodal approach for assessing learners’ interaction experience. ICMI ’13. Dec. 9 – 13, 2013. Sydney, Australia. https://doi.org/10.1145/2522848.2522896. 271 – 278, 2013.
Kear K, Chetwynd F, and Jefferis H. Knowing me, knowing you: Personal profiles in online learning. eLearn Magazine. 1 – 7. 2013.
Kizilcec RF, Piech C, and Schneider E. Deconstructing disengagement: Analyzing learner subpopulations in Massive Open Online Courses. In LAK ’13. Leuven, Belgium. 170 – 179, 2013.
Korchi A, Elidrissi NE, Jeghal A, Oughdir L, & Messaoudi F. A modeling learner approach in a computing environment for human leaning based on ontology. Int. J. Comput. Eng. Technol 6: 21-31, 2013.
Liegle JO, and Janicki TN. The effect of learning styles on the navigation needs of Web-based learners. Computers in Human Behavior 22: 885 – 898, 2006.
Mäntysaari M. Ambiguity tolerance as an instrument of learner profiling: A Q methodological study of how upper secondary school students’ perceptions of EFL reading reconstruct a learner variable. Master’s thesis. University of Jyväskylä. 1 – 136, 2013.
May E, Taylor C, Peat M, Barko AM, and Quinnell R. An application of student learner profiling: Comparison of students in different degree programs. In the proceedings of UniServe Science Assessment Symposium: 89 – 96, 2012.
McLoughlin C. Inclusivity and alignment: Principles of pedagogy, task and assessment design for effective cross-cultural online learning. Distance Education 22: 7 – 29, 2001. https://doi.org/10.1080/0158791010220102.
Montero CS, and Suhonen J. Emotion analysis meets learning analytics – Online learner profiling beyond numerical data. In Koli Calling ’14. Nov. 20 – 23, 2014. Koli, Finland. 165 – 169, 2014.
Ochoa X. Learnometrics: Metrics for Learning (Objects). In the proceedings of LAK 2011. Retrieved from https://www.slideshare.net/xaoch/learnometrics-keynote-lak2011. 2011.
Pellow AJ, Smith EM, Beggs BJ, and Fernandez-Canque HL. Assessment of a-priori and dynamic extended learner profiling for accommodative learning. In Proceedings of the 9th CAA Conference. Loughborough, Loughborough University. 2005.
Rothwell WJ, and Cookson PS. Beyond Instruction: Comprehensive Program Planning for Business and Education. San Francisco: Jossey-Bass Publishers. 1997.
Salomoni P, Mirri S, Ferretti S, and Roccetti M. Profiling learners with special needs for custom e-learning experiences, a closed case? In W4A ’07 Proceedings of the 2007 international cross-disciplinary conference on Web accessibility. 84 – 92, 2007. Banff, Canada. May 7 – 8, 2007. https://doi.org/10.1145/1243441.1243462 .
Shegog R, Rushing SC, Gorman G, Jessen C, Torres J, Lane TL, Gaston A, Revels TK, Williamson J, Peskin MF, D’Cruz J, Tortoero S, and Markham CM. NATIVE-It’s Your Game: Adapting a technology-based sexual health curriculum for American Indian and Alaska Native youth. J Primary Prevent 38: 27 – 48, 2017. https://doi.org/10.1007/s10935-016-0440-9.
Skourlas C, Sgouropoulou C, Belsis P, Pantziou G, Sfikas C, and Fosses N. Learner profiles In the higher educational context. In Proceedings of E-Ra 2nd International Conference Information Technology to Science, Economy, Society and Education. 2007.
Spencer SJ, Steele CM, and Quinn DM. Stereotype threat and women’s math performance. Journal of Experimental Social Psychology 35: 4 – 28, 1999. https://www.sciencedirect.com/science/article/pii/S0022103198913737.
Steele CM, and Aronson J. Stereotype threat and the intellectual test performance of African Americans. Jrn Personality and Social Psychology 69: 797 – 811, 1995. http://mrnas.pbworks.com/f/claude%20steele%20stereotype%20threat%201995.pdf.
Su AYS, Yang SJH, Hwang W-Y, and Zhang J. A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments. Computers & Education 55: 752 – 766, 2010.
Taraghi B, Saranti A, Ebner M, Müller V, and Groβmann A. Towards a learning-aware application guided by hierarchical classification of learner profiles. Journal Of Universal Computer Science 21: 93 – 109, 2015.
Tsianos N, Lekkas Z, Germanakos P, Mourlas C, and Samaras G. User-centric profiling on the basis of cognitive and emotional characteristics: An empirical study. In: Nejdl W., Kay J., Pu P., Herder E. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2008. Lecture Notes in Computer Science, Vol. 5149. Springer, Berlin, Heidelberg. 214 – 223, 2008. https://doi.org/10.1007/978-3-540-70987-9_24.
Van Biljou J, and Kotzé P. Profiling mLearning students according to cultural dimensions: Is that possible? UNISA Institutional Repository. http://uir.unisa.ac.za/handle/10500/3137. 2007.
Wettasinghe M, and Hasan M. Exploring the efficacy of IT with slow learners: Case studies in primary schools. In Conference ICL 2007. Sept. 26 – 28, 2007. Villach, Austria. 1 – 11, 2007. https://telearn.archives-ouvertes.fr/hal-00197252/document.
Yoon S-Y, Bhat S, and Zechner K. Vocabulary profile as a measure of vocabulary sophistication. In the 7th Workshop of the Innovative Use of NLP for Building Educational Applications. Montréal, Canada. June 3 – 8, 2012. 180 – 189, 2012.
Additional Reading Section
Blake R, and Sekuler R. Perception. 5th Ed. Boston: McGraw Hill. 1985.
Joseph PB, Bravmann SL, Windschitl MA, Mikel ER, and Green NS. Cultures of Curriculum. Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers. 2000.
Sousa DA. How the Brain Learns. 4th Ed. Thousand Oaks, California: Corwin. SAGE Publishing. 2005
Author information
Authors and Affiliations
Key Terms and Definitions
- Cultural profiling
-
Applying an individual or group’s cultural background as a filter through which to understand the target individual or group (with “culture” defined as the collective values, thinking, and practices of peoples at particular times and spaces)
- Data mining
-
The identification of intrinsic patterns in data and information
- Demand-side forecasting
-
Projecting user interest in a product or service based on empirical and other data and research methods
- Demographics
-
Statistical data about human populations and sub-populations, including counts of people by age, race, class, and other factors
- Language profiling
-
The application of native language(s) as part of understanding individuals and groups
- Learner profiling
-
The describing of learners by rough details, usually based around particular dimensions and indicators
- Potential learner
-
A profile of individuals and groups who may find a particular open-shared learning resource of-interest for their own learning
- Profile extraction
-
The uses of user-based log data to describe learners
- Target learner
-
A profile of individuals and groups who have a learning object or sequence built in anticipation of their needs and wants
- Usability
-
Fitness for use
- User model
-
A representation of target learner group’s knowledge related to the domain topic and related topics and their preferences for the learning (in context)
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hai-Jew, S. (2019). Profiling Target and Potential Learners Today and into the Future. In: Designing Instruction For Open Sharing. Springer, Cham. https://doi.org/10.1007/978-3-030-02713-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-02713-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02712-4
Online ISBN: 978-3-030-02713-1
eBook Packages: EducationEducation (R0)