Challenges and opportunities in e-Learning

1. Challenges and opportunities in e-Learning Ioana Moisil1, Iulian Pah2 1 “Lucian Blaga “ University os Sibiu, Romania 2 “Babes Bolyai” University, Cluj-Napoca, Romania ioana.moisil@ulbsibiu.ro ; i_pah@yahoo.com

2. AcknowledgementThis work benefits from funding by the Romanian Ministry of Education, Research and Youth (INFOSOC - CEEX 73/31.07.2006)

3. challenges opportunities

4. Computer Aided Learning (CAL) Computer Aided Training (CAT) Computer Aided Instruction (CAI) Computer Based Learning (CBL) Computer Based Training (CBT) e-Learning

5. Supervised learning

6. Web 2.0 challenges • Google AdSense Flickr • BitTorrent • Napster • Wikipedia • Blogging • upcoming.org and EVDB • search engine optimization • cost per click • web services • Participation • Wikis • tagging ("folksonomy") • syndication

8. .A "meme map" of Web 2.0 (from http://www.oreillynet.com)

9. DANTE Socio-Cultural Models implemented through multi-agent architecture for e-learning

10. Main objective: the development of a model for the virtual education system, student centred, that facilitates the learning through collaboration as a form of social interaction

11. Background • Vygotsky's theory of social cognitive development (1934/1986): described learning as being embedded within social events and occurring as a child interacts with people, objects, and events in the environment • Bandura's social learning theory

13. STUDENT MODEL • an action i is defined by a set of attributes A. An individual has to decide among a set I of possible actions: I= {ik, k=1,2,...,n} and Ak= {aki, i=1,2,....m} (1) • quality-cost function

14. STUDENT MODEL – cont. • At individual level we are considering that the evaluation is also influenced by two categories of factors: beliefs (cognitive) and affects (emotive). ik = ∑i Bi Ei (2) where ik is an action, k=1,..,n Bi is the belief that ik posses the attribute aki, i=1,..,m Ei is the evaluation or utility (desirability) of attribute aki, i=1,..,m

15. Intervention/action ik

16. STUDENT MODEL – cont. • Student attitude toward an act, αk, is the sum of the student's belief strength in the consequences resulting from performing a certain action (taking a certain decision)weightedby the evaluation of an anticipated outcome (positive benefit or avoidance of a negative consequence): αk= ∑iβki εki (3) where αk is the attitude toward an action ik, k=1,..,n βki is the belief that performing ik will lead to an anticipated outcome i, i=1,..,m εki is the evaluation or utility (desirability) of the outcome i, i=1,..,m

17. STUDENT MODEL – cont. • The influence of the colleagues from the learning environment can be modeled by introducing the subjective norm: SN= ∑j NBkj MCkj (4) where SN is the subjective norm - the motivation toward an action ik, k=1,..,n, as determined by the influence of the group NBkj is the normative belief that people from the group (j) expect an individual to perform an action ik will lead to j, j=1,..,n MCkj is the motivation to comply with the expectation of the group (j) j, i=1,..,n

18. The theory of reasoned action • combining the attitude toward an act and the subjective norm: DB = f[(BI) = f (αk)w1 + (SN)w2] (5) where DB is the decisional behavior BI is the behavioral intention αk is the attitude toward performing the action ik SN is the subjective norm w1 and w2 are evaluation weights determined empirically

19. Student cognitive model • Vygotsky zone of proximal development (ZPD) • four learning stages: • assistance provided by more capable others (coaches, experts, teachers); • self assistance; • internalization automatization (fossilization); and • de-automatization: recursiveness through prior stages.

20. Vygotsky's theory also claims "that instruction is most efficient when students engage in activities within a supportive learning environment and when they receive appropriate guidance that is mediated by tools"

22. TUTOR - model The TUTOR assistant evaluates the educational objectives of the student and recommends her/him some kind of activities. The decisions are based on the knowledge of the students’ cognitive profile (which takes into account the social component). The TUTOR agent interacts with the personal assistant of the student, with the mediating agent and with the social agentified environment. As the system is conceived, the accent is put on collaboration activities between students, which consist in knowledge exchange, realization of common projects, tasks’ negotiation, sharing resources, common effort for the understanding of a subject, problem-solving in-group. The TUTOR is mainly evolving in a network populated with learning objects.

23. Ant social behaviour • Pierre-Paul Grass–stigmergy (indirect, non-symbolic form of communication,medisated by the environment; local information) • Deneubourg et al. - studies on pheromone laying and general ant behavior (double bridge experiment) → main source of inspiration for the development of ant colony optimization (ACO)

24. ACO In ACO, a number of artificial ants build solution to an optimization problem and exchange information on their quality via a communication scheme that is reminiscent of the one adopted by real ants.(Dorigo et al., 2006) ACO algorithms use a population of agents-artificial ants in order to build a solution to a discrete optimisation problem. Solutions’ information (individual connections of the problem) are kept in a global memory – the pheromone mapping. Specific heuristics can be considered on a-priori information.

25. 3 processes • ants generation and activity, • pheromone trail evaporation and • daemon actions.

26. TUTOR group-work activities sub-system Virtual learning environment: • a set G of students’ teams and • a set A of group work activities consisting of several tasks. Let ajA with j=1,…,J be an activity from the set A ; (1) giG with i= 1,…I be a team from the set G, and (2) tkjiTK , j=1,…,J and i= 1,…I (3) be a task of the activity aj that has to be performed by the team gj.

27. TUTOR group-work activities sub-system(cont.) activity aj :an ordered sequence of tasks from a set TK = { tkji}. tasktkji : to be performed by the team giin a number djiof time units. N = │TK│- total number of tasks. Goal: to assign tasks to time intervals in such a way that no two activities are performed at the same time by the same team and that the maximum completion time of all tasks is minimized.

28. TUTOR group-work activities sub-system(cont.) A team giG is supposed to have M members (student-agents): gi = gim with m=1,.., M (4) • all teams have the same number of members • there are task-classes • the potential assignment of one student to a specific task is based on the concept of response threshold combined with a function of the background knowledge of the student and her decision making behaviour.

29. TUTOR group-work activities sub-system(cont.) A student-agent, gim , is attracted to a task tji with a probability P depending on her background knowledge, beliefs (cognitive), affects (emotive) and intentions and on response threshold θimj : Pθimj (simj) = simj2/ (simj2 + θimj2) (5) where simj = f (beliefs, affects, intention, qualification-knowledge and skills).

30. TUTOR group-work activities sub-system(cont.) The response threshold θimj of the ant-agent gim to the task tkji is decreased when the agent has performed the task tkji ; at the same time, thresholds for the other tasks are increasing proportional to the time t to perform the task. θimj new= θimjold – xijξΔt + (1-xij) φΔt (6) where: xij is the time spent by the agent i for the task j, ξ is a learning coefficient , and φ is a forgetting coefficient. If a task is performed, the response threshold is decreased with a quantity depending of the learning coefficient, in the opposite situation; the threshold is increased with a function of the forgetting coefficient.

31. TUTOR group-work activities sub-system(cont.) The state of an ant-agent is evolving from “active” – performing a task, to “inactive” –idle, and vice versa. An inactive agent starts to perform a task with a probability P given by (5). An active agent completes the task, or abandons it, with a probability p per time unit : p = probability (state = “active” → state = “inactive”) (7) 1/p is the average time spent by an agent in task performing before giving up the task.

32. TUTOR group-work activities sub-system(cont.) The problem is represented as a weighted graph Q = (TK’, L), where TK’= TK {tk0} and L is the set of edges that connect node t0with the first task of each activity. The vertexes of T are completely connected, with exception of the nodes of the tasks from the same activity that are connected sequentially, each such node being linked only to its direct successor. There are N (N-1)/2 + │A│ edges. Each edge (k, l) is weighted by two numbers: τlk- the pheromone level (trail level) and ηkl– the so called visibility and represents the desirability of a transition from node k to node l. Each ant-agent has associated a data structure – the tabu-list, that memorises the tasks of an activity that have been performed at the time moment t.

33. TUTOR group-work activities sub-system(cont.) A transition probability function from node i to node j for an ant-agent (a-a)k was defined as: (8) where allowedk = (N – tabuk) tabuk is a vector that changes dynamically and contains the tabu-list of the kth ant. άand β are parameters that are used to control the relative importance of pheromone level and visibility

34. TUTOR group-work activities sub-system(cont.) NR - number of potential active agents, NRact - number of active agents at the time t, The variation of the attraction of a task (pheromone deposit) in a discrete time situation: simj (t+1) = simj (t) + β - (άNRact) / NR (9) The order in which the nodes are visited by each ant-agent specifies the proposed solution.

35. Conclusion “We shape our dwellings and afterwards our dwellings shape our lives”. Winston Churchill