Abstract About 80 percent of the world’s Muslim populations are non-native speakers of the Arabic language. Since it is obligatory for all Muslims to recite Qur’an in Arabic during prayers, an extraordinary social phenomenon has taken place in some parts of the Muslim world: Muslims are taught the complex phonological rules of the Arabic language in the context of Qur’an and they recite the “sounds” of Qur’an often understanding very little. This has given rise to a demographic segment of adult learners whose main learning goal is recalling a closed set of syntactic rules and vocabularies in the context of Qur’an while reciting or listening to it so that they can reconstruct a meaning in their native-language. Despite the availability of some resources for learning language for this specific purpose, according to our detailed investigation, no work has explored the possibilities of emerging adaptive and intelligent systems for collaborative learning to address this challenge. The goals of this work are: To determine the applicability of learner corpus research, declarative memory modelling, and social learning motivation on the learners’ specific pedagogical objectives and to use the Design-Based Research methodology (DBR) to optimize the design of such a system in real-life setting to observe how the different variables and elements work out. We present here, a prototype to gather requirement analysis of such a system by bootstrapping a user community. The compiled data were used to design an initial architecture of an intelligent and adaptive Qur’anic Arabic learning system.

References

[1] Abdullah M. and Pathan, A., Learning Qur anic Arabic through Interactive Web-Based Software: A Pragmatic Approach in Language for Specific Purpose, in Proceeding of the 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M), pp. 1-6, 2013.

[2] Al-Yaari S., Al Hammadi F., and Alyami S., Written Grammatical Errors of Arabic as Second Language (ASL) Learners: An Evaluative Study, International Journal of English Language Education, vol. 1, no. 2, pp. 143-166, 2013.

[3] An S., Schema Theory in Reading, Theory and Practice in Language Studies, vol. 3, no. 1, pp. 130-134, 2013.

[4] Baddeley A., Human Memory: Theory and Practice, Psychology Press, 1997. The International Arab Journal of Information Technology, Vol. 14, No. 1, January 2017 88

[5] Bahrick H. and Phelps E., Retention of Spanish Vocabulary Over 8 Years, Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 13, no. 2, pp. 344-349, 1987.

[6] Bergstrom-Lehtovirta J., Oulasvirta A., and Brewster S., The Effects of Walking Speed on Target Acquisition on a Touchscreen Interface, in Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, Sweden, pp. 143-146, 2011.

[7] Boticki I., Wong L., and Looi, C., Designing Technology for Content-Independent Collaborative Mobile Learning, IEEE Transactions on Learning Technologies, vol. 6, no. 1, pp. 14-24, 2013.

[8] Byrnes H., The Role of Listening Comprehension: A Theoretical Base, Foreign Language Annals, vol. 17, no. 4, pp. 317-329, 1984.

[9] Cobb T., Is There any Measurable Learning from Hands-on Concordancing?, System, vol. 25, no. 3, pp. 301-315, 1997.

[10] Collins A., Joseph D., and Bielaczyc K., Design research: Theoretical and Methodological Issues, The Journal of the Learning Sciences, vol. 13, no. 1, pp. 15-42, 2004.

[11] Dukes K., Atwell E., and Habash N., Supervised Collaboration for Syntactic Annotation of Quranic Arabic, Language Resources and Evaluation, vol. 47, no. 1, pp 33- 62, 2013.

[12] Dukes K. and Habash N., Morphological Annotation of Quranic Arabic, in Proceedings of Seventh International Conference on Language Resources and Evaluation, Malta, pp. 2530-2536, 2010.

[13] Edge D., Fitchett S., Whitney M., and Landay J., MemReflex: Adaptive Flashcards for Mobile Microlearning, in Proceedings of MobileHCI 2012 Conference on Human Computer Interaction with Mobile Devices and Services, CA, pp. 431-440, 2012.

[14] Ellis N., The Psychology of Foreign Language Vocabulary Acquisition: Implications for CALL, Computer Assisted Language Learning, vol. 8, no. 2, pp. 103-128, 1995.

[15] Emery P., Collocation in Modern Standard Arabic, Journal of Arabic Linguistics, vol. 23, no. 1, pp. 56-65, 1991.

[16] Flowerdew L., Corpus-based Research and Pedagogy in EAP: From Lexis to Genre, Language Teaching, vol. 48, no. 1, pp. 99-116, 2015.

[17] Forte A. and Bruckman A., Why do People Write for Wikipedia? Incentives to Contribute to Open-Content Publishing, in Proceedings of GROUP, FL, pp. 6-9, 2005.

[18] Ganji M., The Effects on Reading Comprehension of Lexical Collocation Instruction, Subject Matter Knowledge, and Cultural Schema, Theory and Practice in Language Studies, vol. 2, no. 1, pp. 105-112, 2012.

[19] Godwin-Jones R., Emerging Technologies from Memory Palaces to Spacing Algorithms: Approaches to Second-Language Vocabulary learning, Language Learning and Technology, vol. 14, no. 2, pp. 4-11, 2010.

[20] Goverover Y., Hillary G., Chiaravalloti N., Arango-Lasprilla C., and DeLuca J., A Functional Application of the Spacing Effect to Improve Learning and Memory in Persons with Multiple Sclerosis, Journal of Clinical and Experimental Neuropsychology, vol. 31, no. 5, pp. 513-522, 2009.

[21] Grimm P., Teaching Collocation in the Arabic as a Foreign Language Class, Ense anza y aprendizaje de la lengua rabe, pp. 163-177, 2010.

[22] Haapalainen E., Kim S., Forlizzi J., and Dey A., Psycho-Physiological Measures for Assessing Cognitive Load, in Proceedings of the 12th ACM International Conference on Ubiquitous Computing, NY, pp. 301-310, 2010.

[23] Hawwari A., Bar K., and Diab M., Building an Arabic Multiword Expressions Repository, in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Korea, pp. 24-29, 2012.

[24] Heliel M., Collocations and Translation, in Proceedings of the FIT Round Table Professional Arabic Translation and New Technologies, Tanger, pp. 31-50, 1989.

[25] Hollan P., Cognitive Versus Stimulus-Response Theories of Learning, Learning and Behavior, vol. 36, no. 3, pp. 227-241, 2008.

[26] Houle C., the Inquiring Mind, University of Wisconsin Press Madison, 1961.

[27] Hunston S., Corpus Linguistics: Historical Development, the Encyclopedia of Applied Linguistics, 2012.

[28] Ikehara C. and Crosby M., Assessing Cognitive Load with Physiological Sensors, in Proceedings of the 38th Hawaii International Conference on System Sciences, Hawaii, pp. 295-295, 2005.

[29] Jafarpour A., Hashemian M., and Alipour S., A Corpus-based Approach toward Teaching Collocation of Synonyms, Theory and Practice in Language Studies, vol. 3, no. 1, pp. 51-60, 2013.

[30] Johnston L. and Miles L., Assessing Contributions to Group Assignments, Journal Assessment and Evaluation in Higher Education, vol. 29, no. 6, pp. 751-768, 2004. 89 A Web and Software-Based Approach Blending Social Networks for Online Qur anic Arabic Learning

[31] Le T., Learning Lexical Collocations with Concordancing and Scaffolding, University of Groningen, 2011.

[32] Lewis M., The Lexical Approach, Language Teaching Publications Hove, 1993.

[33] Lugo L., The Future of the Global Muslim Population, Pew Research Center, USA, 2011.

[34] Maamouri M., Bies A., Buckwalter T., and Mekki W., The Penn Arabic Treebank: Building a Large-Scale Annotated Arabic Corpus, in Proceedings of NEMLAR Conference on Arabic Language Resources and Tools, pp. 102-109, 2004.

[35] Magnisalis I., Demetriadis S., and Karakostas A., Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field, IEEE Transactions on Learning Technologies, vol. 4, no. 1, pp. 5-20, 2011.

[36] McEnery T. and Xiao R., What Corpora Can Offer in Language Teaching and Learning, Handbook of Research in Second Language Teaching and Learning, vol. 2, pp. 364-380, 2010.

[37] Mendelsohn D. and Rubin j., A Guide for the Teaching of Second Language Listening, CA: Dominie Press, 1995.

[38] Milton J., Measuring Second Language Vocabulary Acquisition, Multilingual Matters, 2009.

[39] Moore L., Learning by Heart in Qur anic and Public Schools in Northern Cameroon, Social Analysis, vol. 50, no. 3, pp 109-126, 2006.

[40] Nakata T., English Vocabulary Learning with Word Lists, Word Cards and Computers: Implications from Cognitive Psychology Research for Optimal Spaced Learning, European Association for Computer Assisted Language Learning, vol. 20, no. 1, pp. 3-20, 2008.

[41] Nation P. and Chung T., 28 Teaching and Testing Vocabulary, The Handbook of Language Teaching, pp. 543, 2009.

[42] ztina, S. Effects of Input Flood and Negative Evidence on Learning of Make/Do Collocations: A Study with Seventh Grade Turkish EFL Students, Master Degree Dissertation, Anadolu University Institute of Educational Sciences, 2009.

[43] Pantelopoulos A. and Bourbakis N., A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis, IEEE Transactions on System, Man, and Cyber, vol. 40, no. 1, pp. 1-12, 2010.

[44] Pavlik P. and Anderson J., An ACT-R Model of Memory Applied to Finding the Optimal Schedule of Practice, in Proceedings of 6th International Conference on Cognitive Modeling 04, USA, 2004.

[45] Pavlik P., Cen H., and Koedinger K., Performance Factors Analysis-a New Alternative to Knowledge Tracing, in Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, Netherlands, pp. 531-538, 2009.

[46] Pavlik P., Presson N., Dozzi G., Wu S., MacWhinney B., and Koedinger K., The FaCT (Fact and Concept Training) System: A New Tool Linking Cognitive Science with Educators, in Proceedings of Cognitive Science Society, Tennessee, pp. 1368-1373, 2007.

[47] Pope N., The Impact of Stress in Self-and Peer Assessment, Assessment and Evaluation in Higher Education, vol. 30, no. 1, pp. 51-63, 2005.

[48] Pyc M. and Rawson K., Testing the Retrieval Effort Hypothesis: Does Greater Difficulty Correctly Recalling Information Lead to Higher Levels of Memory?, Journal of Memory and Language, vol. 60, no. 4, pp. 437-447, 2009.

[49] Rafiq Y. and Fullerton H., Peer Assessment of Group Projects in Civil Engineering, Assessment and Evaluation in Higher Education, vol. 21, no. 1, pp. 69-81, 1996.

[50] Razak Z., Ibrahim N., Idris M., Tamil E., and Yakub M., Quranic Verse Recitation Recognition Module for Support in J-QAF Learning: A Review, International Journal of Computer Science and Network Security, vol. 8, no. 8, pp. 207-216, 2008.

[51] Saif A. and Aziz M., An Automatic Collocation Extraction from Arabic Corpus, Journal of Computer Science, vol. 7, no. 1, pp. 6-11, 2011.

[52] Schilit B., Adams N., and Want R., Context- Aware Computing Applications, in Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, USA, pp. 85-90, 1994.

[53] Short J., Williams E., and Christie B., The social psychology of telecommunications, Wiley, 1976.

[54] Simon H., The Sciences of the Artificial, MIT press, 1996.

[55] Son L. and Simon D., Distributed Learning: Data, Metacognition, and Educational Implications, Educational Psychology Review, vol. 24, no. 3, pp. 379-399, 2012.

[56] Talmon R. and Wintner S., Morphological Tagging of the Qur an, in Proceedings of the Workshop on Finite-state Methods in Natural Language Processing, 10th Conference of the European Chapter of the Association for Computational Linguistics, Haifa, pp. 67-74, 2003.

[57] Thoms B., A Dynamic Social Feedback System to Support Learning and Social Interaction in The International Arab Journal of Information Technology, Vol. 14, No. 1, January 2017 90 Higher Education, IEEE Transactions on Learning Technologies, vol. 4, no. 4, pp. 340- 352, 2011.

[58] Tu C. and McIsaac M., The Relationship of Social Presence and Interaction in Online Classes, The American Journal of Distance Education, vol. 16, no. 3, pp. 131-150, 2002.

[59] Wilson G., Brewster S., Halvey M., Crossan A., and Stewart C., The Effects of Walking, Feedback and Control Method on Pressure-based Interaction, in Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, NY, pp. 147-156, 2011.

[60] Wong W., Input Enhancement: From Theory and Research to the Classroom, McGraw-Hill, 2004.

[61] Wu S., Franken M., and Witten H., Supporting Collocation Learning with a Digital Library, Computer Assisted Language Learning, vol. 23, no. 1, pp. 87-110, 2010.

[62] Yang J. and Park J., Software Review: SuperMemo UX-Extreme English Advanced and Proficient, CALICO Journal, vol. 29, no. 4, pp. 718-726, 2012.

[63] Zylberberg A., Dehaene S., Roelfsema R., and Sigman M., The Human Turing Machine: a Neural Framework for Mental Programs, Trends in Cognitive Sciences, vol. 15, no. 7, pp. 293-300, 2011. Matin Abdullah is pursuing Ph.D. degree in Computer Science at IIUM, Malaysia. He received M.Sc. degree in Computer Science in 2002 from University of Houston, USA. He received B.Sc. degree in Electrical Engineering from the same University. He is also currently an Assistant Professor in Computer Science and Engineering department and Associated Director of Centre for Research on Bangla Language Processing, BRAC University, Bangladesh. Al-Sakib Pathan received Ph.D. in Computer Engineering in 2009 from Kyung Hee University, South Korea and B.Sc. in Computer Science and Information Technology from Islamic University of Technology (IUT), Bangladesh in 2003. He is currently an Assistant Professor at the CS department in IIUM, Malaysia. He is actively involved in numerous research events and journals. He is a Senior Member of IEEE, USA. Imad Al Shaikhli is an Associate Professor at the Department of Computer Science, International Islamic University Malaysia, Malaysia. He is currently serving as the Head of Research (HoR) at the Kulliyyah (Faculty) of Information and Communication Technology. His research interest includes cryptography, steganography, genetic algorithms, neural networks and biometrics. He is a Senior Member of IEEE, USA.