MELMS: A Framework For Web Mining E-Learning Management Systems

MELMS: A Framework For Web Mining E-Learning Management Systems A.Bellaachia & Eswara Vommina Computer Science Department George Washington University Washington, DC-20052

What is E-Learning? • Learning can be accomplished over the internet • A virtual Classroom • Systematic use of network and multimedia technologies • Complex systems • Very good for Professionals

Simple Learning Content is same for all learners Time constraints Not flexible Courses cannot be reused Skill barriers Traditional classroom Complex Learning content may not be same for all learners (Personalized) No Time constraints Very flexible Courses can be reused Skill barriers can be minimized (supportive learning objects) Virtual classroom Traditional Learning Vs E-Learning

Framework • We present a framework for applying data mining techniques on the weblogs of an E-Learning system to improve the learning experience of a learner and also the performance of the E-Learning system.

E-Learning System • E-Learning is a systematic use of networked multimedia, computer technologies to empower learners, improve learning, connect learners to people and resources supportive of their needs. • Definitions: • An E-Learning system (ELS) is a set of Learners (Learning entity), Learning Objects & Learning Paths: ELS = {eL, LO, LP} • An E-Learning system can be represented as a weighted graph in which nodes represent the Learning Objects and the edges represent a hyper-link from one learning object to the other. • Weight- Represents the level of difficulty the Learner faces while moving from one node to another node

E-Learning System Objective:The objective of the ELS is to make an E-Learner reach his learning objective with a minimum score.

Learning Entity A Learner can be described as an entity with a goal. eL = { Knowledge goal – kG (node in the graph) Knowledge state- kS (node in the graph) Profile - kP Score - S Deviation - D } S = ∑ Si where Si- score obtained at quiz ‘i’. • The profile of the learner can be obtained when he registers for the course or when he logs in for the first time

Learning Entity (Cont.) Profile • Based on IEEE Personal and Private Information (PAPI) • Evaluates performance of the learner • Distinguishes from other learners • Measured performance through learning objects is stored in Performance property which is represented by the Score • Portfolio used to represent pervious experience which can be used to obtain the knowledge state • Interactions with the system can be used to find out learner’s interests • A learner accessing a learning object between a start time and an end time can be obtained

Components: Content- Learning Unit (LU) Links- to related materials Quiz-Assessment that tests the skills gained by the learner Practice Exercise-Learner can practice the concepts learned FAQs- Questions asked by previous visited learners Learning Object (LO) Fig.3 Learning Object Links LU Quiz FAQs Practice Exercise

Fig.1 Set of Learning Objects Learning Object (LO) • Each LO is designed to meet a specific Objective. • The Objective of an LO is to make the learner understand the content and move towards the Knowledge Goal. • LO is identified by a URI • LOs can be reused among different courses and Learning Contexts • Rules to reuse a LO are given by the Content Provider • Cost of a component: Time spent on each component. • A set of learning objects form a learning module.

Learning Unit • A digital repository of information that supports Learning • The smallest unit • Can be reused by many Learning Objects • Time to Finish: Expected time to finish the unit. This is provided by the content provider.

FAQ • This component contains the Frequently Asked questions related to the Unit • Case-based reasoning can be used to add new questions and also to present the order of the questions. Questions asked by maximum number of learners are presented first. • Cost: A Count of number of questions that are read by the learner

Quiz • Provided by content provider • Cost: score made by a learner.

Practice • The practice component can be either a case study, a sample exercise etc. • Cost: The time spent at the practice component • The cost gives how interesting the component is to the learner

Learning Path • Sequence of Learning Units (LU) and Quiz (Q) provided to the E-Learner. In this initial phase, we are omitting other components such as FAQs. • A Learning Path represents a learning module. • Cost of a learning path: Cost = Sum over all nodes • Cost of each node (LU or Q): • CLU: It is the sum of “time to finish” for each learning unit in the learning path • Cq: A modified time spent on each quiz. See next slide.

Quiz • Assumption: • We assume that each quiz has a maximum time to finish. • Let • Smax be the maximum score for the quiz • S be the score made by the learner on the quiz • T be the time spent by the learner on the quiz • The cost is computed as follows: • Notes: • If a learner got a 100% score within the time to finish, the cost will be the time spent on the quiz. • If a learner gets less than 100% score, Cq will be higher to indicate that he or she would need more time to finish the quiz.

3 4 5 13 6 12 1 2 7 14 8 11 9 10 Learning Path • Node 2 can be viewed as quiz node. Depending on the score of a learner, the system would recommend either the “blue” path or “orange’ path: • Example: • Compare the Cq with a given threshold (e.g., provided by content provider) and then: If Cq <  value then the learner would follow the “blue” path Else The learner would follow the “orange” path

Supportive Learning Objects • Example course for teaching Computer Graphics • The sequence of LOs is as follows: -Rasterization Techniques -Vectors (can be derived from a Vector calculus course) -Matrices (can be derived from a Mathematics course) - Projections -Light (can be derived from a physics course) -Shading -Graphics Library (can be derived if exists or created new) -C programming (can be derived from an existing course)

Fig.4 WebLog Format IP Address Username Date Time Protocol Method Status Document The Mining Process • Web Usage Mining -Every hit is recorded in the Weblogs • Weblogs of the E-Learning site • Different from commercial sites Weblog analysis -User logins -Browsing spans are not small and lasts for a number of sessions -Objectives are different compared to commercial sites

The Mining Process • Pre-Processing -Request Failures -Image file hits • Sessions -Sessions at the LO level not LU level -All LUs for a LO are placed in onedirectory -Identify hits for each LU, Practice Exercise, Quiz -Total time spent for each LO

Mining Process: Path Traversal Patterns • Expected Learning Paths (ELPs): Can be derived from the e-learning site • Model the site as a graph and find all possible paths (Algorithms do exist than can derive these paths) • Traversed Learning Paths (TLPs) : • Use web log to find path traversal patterns • Analysis of ELPs and TLPs : • Recommendations for e-learners: Deviations from ELPs • Recommendations for Content providers • Recommendations for Site designers

Classification of Learners • Based on profiles • Based on Paths taken • Teachers can design exercises appropriate for each class of learners -Low Mastery -High Mastery

Association Rule Mining • Recommending the next appropriate LO to the learner based on previous learners paths analysis • Associations rules are found based on the learning path taken by the learner • Properties of Association Rules -Support -Confidence

Association Rule Mining • Association rules are of the form X->Y X-Antecedent of the rule Y-Consequent of the rule • Set of Items I ={LU1,LU2,LU3,…} • Sessions D = {S1,S2,S3,….} where Si  I • Itemset = { LUi1,LUi2,LUi3,…} where LUij  I

Association Rule Mining • Support % of transaction that contains the itemset • Confidence Ratio of number of transactions that contains both the antecedent and the consequent, number of transactions that contains all itemsets in the antecedent • Eg: 1) LU1,LU2,LU3 => L8 Learners who shows interest in learning units 1,2 and 3 would show more interest in learning unit 8. 2)LU1,LU2,PE2,LU3,LU4,PE4 => LU6,LU7,LU8 Learners who shows interest in learning units 1, 2 , 3 & 4 and practices exercise 2 & 4 shows more interests in Learning units 6,7, and 8

Visualization • Reports to Teachers -Learner performance -Learner behavior -Paths taken • Reports to Administrators

Conclusion • Framework • Mining Process • Advantages

Thank You

MELMS: A Framework For Web Mining E-Learning Management Systems