Cognitive Knowledge-Based Algorithms for Dynamic Knowledge Representation of Adaptive Feedback
Keywords:
Adaptation, Learning Environment, Domain Modeling, Student Modeling, AlgorithmAbstract
Historically, the creation of knowledge-based systems was perceived as a human transfer of expertise to the developed system. This perspective operated on the assumption that the necessary knowledge already existed and merely needed to be gathered and incorporated. Typically, this involved acquiring knowledge through expert interviews and translating it into production rules. However, this approach encountered challenges in adequately representing diverse knowledge types. The presence of various knowledge types and the lack of robust justifications for the rules rendered the system maintenance time-consuming and arduous. Consequently, this method was primarily viable for constructing prototypes, prompting a transition from the transfer method to the modeling approach. The modeling approach diverges from simulating the entire cognitive process of an expert and instead aims to create a model that produces similar outcomes in problem-solving. While several knowledge modeling techniques for delivering feedback in computer-based learning environments have been proposed, our research indicates that these techniques are often static, involve a manual knowledge elicitation process, and heavily rely on the volatile knowledge of experts. Consequently, there is a pressing need to streamline this process with a dynamic approach to knowledge representation in an adaptive feedback environment. This research seeks to introduce and assess the performance of knowledge elicitation, knowledge bonding, and adaptive feedback algorithms in representing knowledge for adaptive feedback. The proposed strategy utilizes the Cognitive Knowledge Base (CKB) to formalize knowledge based on an Object-Attribute-Relation (OAR) model. This technique empowers the CKB to autonomously decide on the type of feedback to provide. Conclusions drawn from the recommendations of the adaptive feedback algorithm align with prior research affirming the appropriateness of feedback in specific scenarios.
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Agarwal, B., Mittal, N., Bansal, P., Garg, S., 2015a. Sentiment analysis using common-sense and context information. Computational intelligence and neuroscience 2015, 30.
Agarwal, B., Poria, S., Mittal, N., Gelbukh, A., Hussain, A., 2015b. Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cognitive Computation 7, 487–499.
Alhajri, R. A. Al-Sharhan, S. Al-Hunaiyyan, A. Alothman, T. (2011). Design of educational multimedia interfaces: individual differences of learners. Proceedings of the Second Kuwait e-Services and e-Systems Conference. April 5-7, 2011. Kuwait.
Al-Hunaiyyan, A. Al-Sharhan, S. (2009). The Design of Multimedia blended e-learning Systems: Cultural Considerations. Proceeding of the 3rd International Conference on Singals, Circuits and Systems, November 6-8, 2009. Djerba, Tunisia. https://ieeexplore.ieee.org/document/5412342/
Al-Hunaiyyan, A., Bimba, A. T., Idris, N., & Al-Sharhan, S. (2017). A cognitive knowledge-based framework for social and metacognitive support in mobile learning. Interdisciplinary Journal of Information, Knowledge, and Management (IJIKM), Volume 12, PP. 75-98. Retrieved from http://www.informingscience.org/Publications/3670
Al-Hunaiyyan, Alhajri, R. Bimba, A. (2021). Towards an Efficient Integrated Distance and Blended earning Model: How to Minimise the Impact of COVID-19 on Education. International International Journal of Interactive Mobile Technologies (iJIM). Vol. 15, No. 10, 2021.
Al-Hunaiyyan, A. Al-Sharhan, S. Alhajri, R. (2020). Prospects and Challenges of Learning Management Systems in Higher Education. International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 11, No. 12, PP. 73-79. December, 2020
Al-Sharhan, S. Al-Hunaiyyan, A. Gueaieb, W. (2006). Success Factors for an Efficient Blended eLearning. Proceeding of the 10th IASTED Internet and Multimedia Systems and Applications (IMSA 2006) Conference. 14/8/2006 - 16/8/2006 Honolulu, Hawaii, USA. The International Association of Science and Technology for Development (IASTED), ACTA Press. PP. 77–82.
Al-Sharhan, S. Al-Hunaiyyan, A. (2012). Towards an Effective Integrated E-Learning System: Implementation, Quality Assurance and Competency Models. Proceedings of The Seventh International Conference on Digital Information Management (ICDIM 2012). 22-24 August 2012. Macau.
Baker, C.F., 2012. Framenet, current collaborations and future goals. Language Resources and Evaluation 46, 269–286.
Baker, C.F., 2014. Framenet: A knowledge base for natural language processing, in: Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore, pp. 1–5.
Banerjee, J.S., Jones, K.O., Williams, D., 2001. Design considerations for a model reference fuzzy adaptive controller. Transactions of the Institute of Measurement and Control 23, 141–162.
Bicocchi, N., Castelli, G., Mamei, M., Zambonelli, F., 2011. Augmenting mobile localization with activities and common sense knowledge, in: International Joint Conference on Ambient Intelligence, Springer. pp. 72–81.
Bimba, A.T., Idris, N., Al-Hunaiyyan, A., Mahmud, R.B., Abdelaziz, A., Khan, S., Chang, V., 2016. Towards knowledge modeling and manipulation technologies: A survey. International Journal of Information Management 36, 857 – 871. doi:http://dx.doi.org/10.1016/j.ijinfomgt. 2016.05.022.
Bimba, A.T., Idris, N., Mahmud, R.B., Al-Hunaiyyan, A., 2017A. A Cognitive Knowledge-based Framework for Adaptive Feedback. Springer International Publishing, Cham. pp. 245–255. doi:10.1007/978-3-319-48517-1_22.
Bimba, A. T., Idris, N., Al-Hunaiyyan, A., Mahmud, R. B., & Shuib, N. L. B. M. (2017B). Adaptive feedback in computer-based learning environments: a review. Adaptive Behavior, DOI: https://doi.org/10.1177/1059712317727590. SAGE Journals.
Bimba, A. Norisma Idris, Al-Hunaiyyan, A. Salwa Ungku Ibrahim, Naharudin Mustafa, Izlina Supa’at, Norazlin Zainal and Mohd Yahya Ahmad. (2021). “The Effects of Adaptive Feedback on Student’s Learning Gains”. International Journal of Advanced Computer Science and Applications (IJACSA), 12(7), 2021. Page 68-80.
Casado, A.G., Marchal, P.C., Ortega, J.G., Garca, J.G., 2019. Visualization and interpretation tool for expert systems based on fuzzy cognitive maps. IEEE Access 7, 6140–6150.
Clariana, R.B., 1990. A comparison of answer until correct feedback and knowledge of correct response feedback under two conditions of contex-tualization. Journal of Computer-Based Instruction .
Demick, J., 2014. Group embedded figures test: Manual. Menlo Park, ca: Mind Garden, Inc .
Driankov, D., Hellendoorn, H., Reinfrank, M., 2013. An introduction to fuzzy control. Springer Science & Business Media.
Fellbaum, C., 1998. WordNet. Wiley Online Library.
Fensel, D., 2003. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. 2 ed., Springer-Verlag New York, Inc., Secaucus, NJ, USA.
Gaynor, P., 1981. The effect of feedback delay on retention of computer-based mathematical material. Journal of Computer-Based Instruction 8, 28–34.
Graesser, A.C., McNamara, D.S., VanLehn, K., 2005. Scaffolding deep comprehension strategies through point&query, autotutor, and istart. Educational psychologist 40, 225–234.
Guo, X., Yang, Y., 2018. Effects of corrective feedback on efl learner’s acquisition of third-person singular form and the mediating role of cognitive style. Journal of psycholinguistic research , 1–18.
Kerr-Wilson, J., Pedrycz, W., 2016. Design of rule-based models through information granulation. Expert Systems with Applications 46, 274–285. Khatib, M., Hosseinpur, R.M., 2011. On the validity of the group embedded figure test (geft). Journal of Language Teaching & Research 2. Kung, C., Su, J., 2007. Affine takagi-sugeno fuzzy modelling algorithm by fuzzy c-regression models clustering with a novel cluster validity
criterion. IET Control Theory & Applications 1, 1255–1265.
Liu, G., Wang, Y., Wu, C., 2010. Research and application of geological hazard domain ontology, in: Geoinformatics, 2010 18th International Conference on, pp. 1–6. doi:10.1109/GEOINFORMATICS.2010.5567498.
M, S., Leclercq, E., Naubourg, P., 2015. eClims: an extensible and dynamic integration framework for biomedical information systems. Ieee Journal of Biomedical and Health Informatics PP, 1. doi:10.1109/JBHI.2015.2464353.
Mason, B.J., Bruning, R., 2001. Providing feedback in Computer-Based Instruction: What the research tells us. Technical Report. University of Nebraska.
Mazzuto, G., Stylios, C., Bevilacqua, M., 2018. Hybrid decision support system based on dematel and fuzzy cognitive maps. IFAC-PapersOnLine 51, 1636–1642.
Michael, N., 2005. Artificial intelligence a guide to intelligent systems. ISBN 321204662, 1–18.
Moreno, R., 2004. Decreasing cognitive load for novice students: Effects of explanatory versus corrective feedback in discovery-based multimedia. Instructional science 32, 99–113.
Puerto, E., Aguilar, J., Lapez, C., Chavez, D., 2019. Using multilayer fuzzy cognitive maps to diagnose autism spectrum disorder. Applied Soft Computing 75, 58–71.
Ramirez, C., Valdes, B., 2012. A general knowledge representation model of concepts. INTECH Open Access Publisher. Ruppenhofer, J., Ellsworth, M., Petruck, M.R., Johnson, C.R., Scheffczyk, J., 2006. Framenet ii: Extended theory and practice.
Salmeron, J.L., Mansouri, T., Moghadam, M.R.S., Mardani, A., 2019. Learning fuzzy cognitive maps with modified asexual reproduction optimi-sation algorithm. Knowledge-Based Systems 163, 723–735.
Sánchez, D., 2010. A methodology to learn ontological attributes from the web. Data & Knowledge Engineering 69, 573–597. Shahinmoghaddam, M., Nazari, A., Zandieh, M., 2018. Ca-fcm: Towards a formal representation of expert’s causal judgements over con-
struction project changes. Advanced Engineering Informatics 38, 620–638.
Speer, R., Havasi, C., 2012. Representing general relational knowledge in conceptnet 5., in: LREC, pp. 3679–3686.
Studer, R., Benjamins, V.R., Fensel, D., 1998. Knowledge engineering: principles and methods. Data & knowledge engineering 25, 161–197. Valipour, M., Yingxu, W., 2015. Formal properties and rules of concept algebra, in: Cognitive Informatics & Cognitive Computing (ICCI*CC),
2015 IEEE 14th International Conference on, pp. 128–135. doi:10.1109/ICCI-CC.2015.7259376.
Wandmacher, T., Ovchinnikova, E., Mönnich, U., Michaelis, J., Kühnberger, K.U., 2011. Adaptation of ontological knowledge from structured textual data, in: Modeling, Learning, and Processing of Text Technological Data Structures. Springer, pp. 129–153.
Wang, Y., 2015a. Concept algebra: A denotational mathematics for formal knowledge representation and cognitive robot learning. Journal of Advanced Mathematics and Applications 4, 61–86.
Wang, Y., 2015b. Towards the abstract system theory of system science for cognitive and intelligent systems. Complex & Intelligent Systems 1, 1–22.
Wielinga, B.J., Schreiber, A.T., Breuker, J.A., 1992. Kads: A modelling approach to knowledge engineering. Knowledge acquisition 4, 5–53. Witkin, H.A., Oltman, P.K., Raskin, E., Karp, S.A., 1971. Group embedded figures test manual. Mind Garden, Inc., Redwood City, CA .
Ye, J., Stevenson, G., Dobson, S., 2011. A top-level ontology for smart environments. Pervasive and Mobile Computing 7, 359–378.
Yurin, A.Y., Dorodnykh, N.O., Nikolaychuk, O.A., Grishenko, M.A., 2018. Designing rule-based expert systems with the aid of the model-driven development approach. Expert Systems , e12291.
Zhang, Y., Qin, J., Shi, P., Kang, Y., 2019. High-order intuitionistic fuzzy cognitive map based on evidential reasoning theory. IEEE Transactions on Fuzzy Systems 27, 16–30.
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