Application of Intelligent Technologies in Computer Engineering Education

1. Application of Intelligent Technologies in Computer Engineering Education Assoc. Prof.Dr.EugenijusKurilovas Vilnius University Institute of Mathematics and Informatics IFIP TC3 Conference.Vilnius.2 July, 2015

2. Introduction What learning content, methods and technologies are the most suitable to achieve better learning quality and efficiency? In Lithuania, we believe that there is no correct answer to this question if we don’t apply personalised learning approach. We strongly believe that “one size fits all” approach doesn’t longer work in education. It means that, first of all, before starting any learning activities, we should identify students’ personal needs: their preferred learning styles, knowledge, interests, goals etc. After that, teachers should help students to find their suitable (optimal) learning paths: learning methods, activities, content, tools, mobile applications etc. according to their needs. But, in real schools practice, we can’t assign personal teacher for each student. This should be done by intelligent technologies. Therefore, we believe that future school means personalisation plus intelligence. In this presentation, Lithuanian Intelligent Future School (IFS) project is presented aimed at implementing both learning personalisation and educational intelligence.

3. Outline • Related EU-funded “Future Classroom Lab” projects • IFS concept and implementation vision: research and development, application and validation of intelligent technologies in education • IFS related R&D works already done • Conclusion

4. Related “Future Classroom Lab” projects

5. http://itec.eun.org/ iTEC (Innovative Technologies for Engaging Classrooms): 2010-2014, 7FP How did the iTEC approach impact on learners and learning: • Key finding 1: Teachers perceived that the iTEC approach developed students’ 21st century skills, notably independent learning; critical thinking, real world problem solving and reflection; communication and collaboration; creativity; and digital literacy. Their students had similar views. • Key finding 2: Student roles in the classroom changed; they became peer assessors and tutors, teacher trainers, co-designers of their learning and designers/producers. • Key finding 3: Participation in classroom activities underpinned by the iTEC approach impacted positively on students’ motivation. • Key finding 4: The iTEC approach improved students’ levels of attainment, as perceived by both teachers (on the basis of their assessment data) and students.

6. http://lsl.eun.org/ LSL (Living Schools Lab): 2012-2014, 7FP With the participation of 15 partners, including 12 education ministries, LSL project promoted a whole-school approach to ICT use, scaling up best practices in the use of ICT between schools with various levels of technological proficiency. The participating schools were supported through peer-exchanges in regional hubs, pan-European teams working collaboratively on a number themes, and a variety of opportunities for teachers' ongoing professional development. Observation of advanced schools in 12 countries produced a report and recommendations on the mainstreaming of best practice, and the development of whole-school approaches to ICT.

7. http://creative.eun.org/ CCL (Creative Classrooms Lab, CCL): 2013-2015, LLP CCL brought together teachers and policy-makers in 8 countries to design, implement and evaluate 1:1 tablet scenarios in 45 schools. CCL produced learning scenarios and activities, guidelines and recommendations to help policy-makers and schools to take informed decisions on optimal strategies for implementing 1:1 initiatives in schools and for the effective integration of tablets into teaching and learning. The 1:1 computing paradigm is rapidly changing, particularly given the speed with which tablets from different vendors are entering the consumer market and beginning to impact on the classroom. Over the next 2-3 years policy makers will face some difficult choices: How to invest most efficiently in national 1:1 computing programmes? What advice to give to schools that are integrating tablets? To address these challenges, CCL carried out a series of policy experimentations to collect evidence on the implementation, impact and up-scaling of 1:1 pedagogical approaches using tablets. Lessons drawn from the policy experimentations also: • Provide guidelines, examples of good practice and a training course for schools wishing to include tablets as part of their ICT strategy. • Support capacity building within Ministries of Education and regional educational authorities and encourage them to introduce changes in their education systems. • Enable policy makers to foster large-scale uptake of the innovative practice that is observed during the project.

8. IFS concept

9. ___________________________________________

10. Future school means personalisation plus intelligence IFS implementation stages (based on iTEC schools innovation maturity model): • Creating learners’ models (profiles) based on their learning styles and other particular needs • Interconnecting learners’ models with relevant learning components (learning content, methods, activities, tools, apps etc.) and creating corresponding ontologies • Creating intelligent agents and recommender systems • Creating and implementing personalised learning scenarios (e.g. in STEM – Science, Technology, Engineering and Mathematics – subjects) • Creating educational multiple criteria decision making models and methods

11. Personalisation

12. Personalisation: creating students’ profiles • Selecting good taxonomies (models) of learning styles, e.g., (Felder & Silverman, 1988), (Honey & Mumford, 2000), the VARK style (Fleming, 1995) • Creating integrated learning style model which integrates characteristics from several models. Dedicated psychological questionnaire(s) • Creating open learning style model • Using implicit (dynamic) learning style modelling method (5) Integrating the rest features in the student profile (knowledge, interests, goals)

13. Personalisation: identifying learning styles

14. Personalisation: identifying learning styles VARK inventory was designed by Fleming in 1987 and is an acronym made from Visual, Aural, Read/write and Kinaesthetic. These modalities are used for preferable ways of learning (taking and giving out) information: • Visual learners prefer to receive information from depictions in figures: in charts, graphs, maps, diagrams, flow charts, circles, hierarchies, and others. It does not include pictures, movies and animated websites that belong to Kinaesthetic. • The aural perceptual mode describes a preference for spoken or heart information. Aural learners learn best by discussing, oral feedback, email, chat, discussion boards, and oral presentations. • Read/write learners prefer information displayed as words: quotes, lists, texts, books, and manuals. • The kinaesthetic perceptual mode describes a preference for reality and concrete situations. They prefer videos, teaching others, pictures of real things, examples of principles, practical sessions, and others. Multimodals are those learners who have preferences in more than one mode.

15. Creating recommender system

16. Creating recommender system

17. Creating recommender system

18. Creating recommender system Interconnection of Activists Brainstorming learning activity with suitable apps and LOs types

19. Creating recommender system

20. Example: Integrating Web 2.0 tools into learning activities

21. Recommender systems (as a kind of services in the e-learning environment) can provide personalised learning recommendations to learners. Recommender systems are information processing systems that gather various kinds of data in order to create their recommendations. The data are primarily about the items (objects that are recommended) to be suggested and the users who will receive these recommendations. The data can be formalised in domain ontology, thus the knowledge about a user and items becomes reusable for people and software agents. Also, the ontology could contain a useful knowledge that can be used to infer more interests than can be seen by just an observation. The aim of TEL is to improve learning. It is therefore an application domain that generally covers technologies that support all forms of learning activities. An important activity in TEL is search-ability relevant learning resources and services as well as their better finding. Recommender systems support such an information retrieval.

22. There are different types of recommender systems based on the recommendation approaches: content-based, collaborative filtering, demographic, knowledge-based, community-based, utility-based, hybrid, and semantic. In this research, knowledge-based recommender system using rules-based reasoning is used. Knowledge-based systems recommend items based on the specific domain knowledge about how certain item features satisfy users’ needs and preferences as well as how the item is useful for the user. Knowledge-based recommender systems can be rule-based or case-based. The form of data collected by the knowledge-based system about user’s preferences can be statements, rules, or ontologies. The knowledge base of the rule-based system comprises the knowledge that is specific to the domain of the application. The rule-based reasoning system represents knowledge of the system in terms of a bunch of rules (facts). These rules are in the form of IF THEN rules such as “IF some condition THEN some action”. If the ‘condition’ is satisfied, the rule will take the ‘action’.

23. The proposed method for Web 2.0 tools integration into learning activities is based on the ontology developed. With the view to find a particular Web 2.0 tool suitable for the accomplishment of the learning activity, a link between the tool and the learning activity must be identified. This relationship can be established by interconnections between the defined tool and activity elements. The learning activity is defined as consisting of the following elements: • Learning Activity (what action a learner performs); • Content (which object a learner manages); • Interaction (with whom a learner interacts); and • Synchronicity (at what time a learner performs the intended action). Web 2.0 tool is defined as set of universal functions. This universal function is defined as consisting of the following elements: • Function (what action can be performed by using a tool); • Artefact (which object can be managed by using a tool); • Interaction (what kind of interaction the tool enables); and • Synchronicity (at what time the intended action is enabled by a tool to take place).

24. The Learning activities and Functions of tools are classified mostly based on the [Conole, 05] media taxonomy. These types and particular elements are presented in Table 2: Table 2: Learning activities and Web 2.0 tools functions types

25. Thus, Web 2.0 tools could be divided based on their usage possibilities, managed objects, communication form, and sort of imitation process into three groups as follows: (1) Artefacts management, (2) Communication, and (3) Imitation tools. We have defined the following components in the domain ontology visualised with Protégé 4.3 ontology editor: Concepts (Main Classes) (Figure 1), and Relationships between Concepts (Properties) (Figure 2):

26. The stages of the method of integrating Web 2.0 tools into learning activities are as follows: • Identification of learner’s learning style (i.e. preferences of the learning content and communication modes) • Selection of the learning objective and the learning method • Determination of the elements of chosen learning method activities • Determination of universal function elements of each Web 2.0 tool • Finding of the link between tool and learning activity elements • Selection of a suitable tool based on specified elements: Action, Interaction, Synchronicity. Artefact is determined based on individual learning style. Description of each stage and the detailed presentation of the method are provided in [Juskeviciene, Kurilovas, 14].

27. In order to ascertain the suitability of this approach, the recommender system prototype was developed. This prototype was developed following the working principles of the knowledge-based recommender system. The domain knowledge was conceptualised in the ontology. The prototype of the knowledge-based recommender system implements this method completely: Scheme of the recommender system

28. Recommender system prototype operation

29. Example: educational multiple criteria decision making

30. Multiple Criteria Decision Making Scalarisation method: the experts’ additive utility function The major is the meaning of the utility function the better LOs meet the quality requirements in comparison with the ideal (100%) quality According to scalarisation method, we need LOs evaluation criteria ratings (values) and weights

31. Linguistic variables conversion into triangle non-fuzzy values and weights: Linguistic variables Non-fuzzy values Excellent/Extremely valuable 0.850 Good / Very valuable 0.675 Fair / Valuable 0.500 Poor / Marginally valuable 0.325 Bad / Not valuable 0.150

32. In identifying quality criteria for the decision making, the following considerations are relevant to all multiple criteria decision making approaches: • Value relevance • Understandability • Measurability • Non-redundancy • Judgmental independence • Balancing completeness and conciseness • Operationality • Simplicity versus complexity

34. Papers 2015 • Kurilovas, E.; Juskeviciene, A.; Bireniene, V. (2015). Research on Mobile Learning Activities Using Tablets. In: Proceedings of the 11th International Conference on Mobile Learning (ML 2015). Madeira, Portugal, March 14–16, 2015, pp. 94–98. • Kurilovas, E.; Zilinskiene, I.; Dagiene, V. (2015). Recommending Suitable Learning Paths According to Learners’ Preferences: Experimental Research Results. Computers in Human Behavior – in print, doi:10.1016/j.chb.2014.10.027 [Q1] • Kurilovas, E.; Juskeviciene, A. (2015). Creation of Web 2.0 Tools Ontology to Improve Learning. Computers in Human Behavior – in print, doi:10.1016/j.chb.2014.10.026 [Q1] • Kurilovas, E.; Vinogradova, I.; Kubilinskiene, S. (2015). New MCEQLS Fuzzy AHP Methodology for Evaluating Learning Repositories: A Tool for Technological Development of Economy. Technological and Economic Development of Economy– in print [Q1] • Kurilovas, E. (2015). Future School: Personalisation plus Intelligence. Chapter in: “Handbook of Research on Information Technology Integration for Socio-Economic Development”. IGI Global – in print

35. Papers 2014 • Kurilovas, E.; Juskeviciene, A.; Kubilinskiene, S.; Serikoviene, S. (2014). Several Semantic Web Approaches to Improving the Adaptation Quality of Virtual Learning Environments. Journal of Universal Computer Science, Vol. 20 (10), 2014, pp. 1418–1432. • Kurilovas, E.; Kubilinskiene, S.; Dagiene, V. (2014). Web 3.0 – Based Personalisation of Learning Objects in Virtual Learning Environments. Computers in Human Behavior, Vol. 30, 2014, pp. 654–662. [Q1] • Kurilovas, E.; Zilinskiene, I.; Dagiene, V. (2014). Recommending Suitable Learning Scenarios According to Learners’ Preferences: An Improved Swarm Based Approach. Computers in Human Behavior, Vol. 30, 2014, pp. 550–557. [Q1] • Kurilovas, E.; Serikoviene, S.; Vuorikari, R. (2014). Expert Centred vs Learner Centred Approach for Evaluating Quality and Reusability of Learning Objects. Computers in Human Behavior, Vol. 30, 2014, pp. 526–534. [Q1] • Juskeviciene, A.; Kurilovas, E. (2014). On Recommending Web 2.0 Tools to Personalise Learning. Informatics in Education, Vol. 13 (1), 2014, pp. 17–30 • Kurilovas, E. (2014). Research on Tablets Applications for Mobile Learning Activities. Journal of Mobile Multimedia, Vol. 10 (3&4), 2014, pp. 182–193.

36. Papers 2013 • Kurilovas, E.; Serikoviene, S. (2013). New MCEQLS TFN Method for Evaluating Quality and Reusability of Learning Objects. Technological and Economic Development of Economy, Vol. 19 (4), 2013, pp. 706–723. [Q1] • Kurilovas, E.; Zilinskiene, I. (2013). New MCEQLS AHP Method for Evaluating Quality of Learning Scenarios. Technological and Economic Development of Economy, Vol. 19 (1), 2013, pp. 78–92. [Q1] • Kurilovas, E. (2013). MCEQLS Approach in Multi-Criteria Evaluation of Quality of Learning Repositories. Chapter 6 in the book: José Carlos Ramalho, Alberto Simões, and Ricardo Queirós (Ed.) “Innovations in XML Applications and Metadata Management: Advancing Technologies”. IGI Publishing, USA, 2013, pp. 96–117. • Kurilovas, E.; Serikoviene, S. (2013). On E-Textbooks Quality Model and Evaluation Methodology. International Journal of Knowledge Society Research, Vol. 4 (3), 2013, pp. 66–78.

37. Papers 2012 • Kurilovas, E.; Zilinskiene, I. (2012). Evaluation of Quality of Personalised Learning Scenarios: An Improved MCEQLS AHP Method. International Journal of Engineering Education, Vol. 28 (6), 2012, pp. 1309–1315. • Kurilovas, E.; Serikoviene, S. (2012). New TFN Based Method for Evaluating Quality and Reusability of Learning Objects. International Journal of Engineering Education, Vol. 28 (6), 2012, pp. 1288–1293. • Zilinskiene, I.; Dagiene, V.; Kurilovas, E. (2012). A Swarm-based Approach to Adaptive Learning: Selection of a Dynamic Learning Scenario. In: Proceedings of the 11th European Conference on e-Learning (ECEL 2012). Groningen, the Netherlands, October 26–27, 2012, pp. 583–593.

38. IFS concept implementation vision

39. Collaboration agreements between Vilnius University and (20 pilot) schools on IFS implementation • Joint expert group on creating interconnections and intelligent agents • R&D, creation of technologies and scenarios, and validation at schools • Feedback, questionnaires, interviews, data mining • Return to (3) based on (4)

40. Conclusion

41. Future school means personalisation + intelligence • Learning personalisation means creating and implementing personalised learning paths based on recommender systems and personal intelligent agents suitable for particular learners according to their personal needs • Educational intelligence means application of intelligent technologies and methods enabling personalised learning to improve learning quality and efficiency • Lithuanian IFS project is aimed at implementing both learning personalisation and educational intelligence

42. Welcome to collaborate. Thank you for your attention. Questions? Dr. Eugenijus Kurilovashttp://eugenijuskurilovas.wix.com/my_site