A Learner Model for Learning Object Based Personalized Learning ...

A Learner Model for Learning Object Based Personalized Learning Environments Galip Kaya and Arif Altun Hacettepe University, Computer Education and Instructional Technology, Ankara, Turkey {galipk,altunar}@hacettepe.edu.tr

Abstract. A personalized learning experience is possible with the use of learner models in intelligent tutoring systems, adaptive educational hypermedia systems and semantic web based learning environments. Although there are standards to overcome complexities or lack of fulfilling user needs (such as, IEEE or IMS), these models are either too generic or too complex to handle. In addition, there are various alternative learner or user models in the literature. Yet, these models do not address how to model learning object (LO) based instructional systems. Therefore, in this paper, an ontology based learner model is proposed for e-learning systems which use instructional learning objects. Keywords: Learner model, learning object, ontology, personalization.

1 Introduction Personalized learning removes time, location and other constraints in teaching and tailors teaching for each learner’s constantly changing needs and skills [1]. In another definition, personalization is described as adapting learning experience to different learners due to the analysis of knowledge, skills and learning preferences of individuals [2]. Concept of personalized learning has changed tradition of “one design for all” of the traditional learning environments. In learning environments, instructional design has evolved from “one instructional design for many learners” to “one design for one learner” or “many designs for one learner”. In traditional learning environments, materials are prepared for an average learner. But in personalized learning environments, materials can be adapted due to academic records, psychological attributes, skills, learning environment preferences of the learner. Content knowledge to be learned can be made more complicated or simpler according to the needs and the demands of the learner [3]. Personalized learning experience is possible for the learners in modern e-learning systems like intelligent tutoring systems, adaptive educational hypermedia systems, adaptive educational systems, semantic web based education systems. The underlying technology of these systems is the use of a learner model, a learner profile or a user model where the information of learner goals, preferences or needs is kept in learner data [4]. A learner model consists of meta-knowledge which includes the instructional decisions about a learner. So a learner model can be defined as an abstract image of the learner in the system [5]. E. García-Barriocanal et al. (Eds.): MTSR 2011, CCIS 240, pp. 349–355, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Although there are standards suggested by consortiums like IEEE (Personal and Private Information, PAPI) or IMS (Learner Information Package, LIP) for learner models, complexities in application of the model is articulated by researchers due to the details in standards or different user needs that cannot be fulfilled by these standards; hence, researchers have proposed new learner or user models for educational or other adaptive systems [6], [7], [8], [9], [10], [11]. The use of LOs for educational purposes brings various advantages, including reusability of the content knowledge, and preparing educationally sound content and delivery. It is expected that a learning environment is to handle the standards with existing LOs. Having considered the constraints that standards bring, an applicable learner model with LO-based systems could not be found in the reviewed literature. For this reason, in this paper, an ontology based learner model is proposed for elearning systems that use learning objects.

2 Literature Review In personalized learning environment literature, personalization is well separated in types by Martinez [3] according to its complexity parameter; personalization is divided into five groups: 1. 2.

3.

4.

5.

Name based personalization: System addresses user by his/her name when user logs into system by username and password. Self-described personalization: System takes user’s preferences, attributes and past experiences by tools like questionnaires, pre-tests and registration forms. Segmented personalization: Learners are grouped by common attributes (class, department, degree etc.) and demographic information. In this method, teaching is applied to whole group. Cognitive personalization: In this method, content and teaching is delivered according to cognitive process, strategy, skill and preferences of learners. System will adapt content by user’s working memory capacity, user’s preference of text or image based representation etc. Whole-person personalization: This method is a combination of cognitive based personalization and psychological resources that affect learning and performance. In this method, the system inferences over user model in learning process and constantly updates user model. So, user can be represented in all aspects.

In user model literature, models generally have similar constructs as described in [3]. Most of the researchers suggest personalization should be in whole-person personalization. On the other hand, segmented personalization is defined as stereotyping and this approach is either advocated or criticized in the literature. It is criticized because of stereotyping of users is an error-prone process. In literature, there are different suggestions to keep which data will be kept and how it is kept:

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Devedzic [2] suggests that IMS LIP and IEEE PAPI standards are a good starting point but they may not be sufficient for all requirements of the system. Researcher suggests to keep the following information in user model: 1. 2. 3. 4.

Objective data: It is provided by learner. e.g. learning history, learning preferences Subjective data: This is related with cognitive personalization attributes. This data is kept by system and frequently updated. Learner performance, and Learning history

Devedzic’s [2] research is an example for using ontologies in learner models. In this research, an ontology based personalized learning environment named TANGRAM is exemplified. Although researcher suggests that by using TANGRAM, it is possible to provide a better personalization via breaking learning objects into pieces to be used via ontologies. This method is limited with only MS Word and Powerpoint based learning objects. This limitation will cause problems in general validity and usability of system. Razmerita et al. [6] developed OntobUM, which is ISM LIP based user modeling system for knowledge management systems. Similar to other user modeling studies, the system uses both users’ self-reported data and information that emerges in usersystem interaction. Another ontology based user model for knowledge management systems is proposed in [12]. In this model, data about user is classified due to related domain and defined as domain-dependent (data special to related domain) and domain-independent (data special to user). The proposed models may be a basis for a learner model, but there are many deficiencies like modeling learning goals, recording learner progress etc. when using these models in adaptive learning environments. Vogiatzis et al. [13] proposed a user model for adaptive hypermedia systems. Researchers listed data to be stored as: Demographic data about user, learner goals, learner preferences and system usage data. Researchers also suggested that user should be placed in a stereotype like beginner, intermediate or advanced. As stereotyping is not a flexible technique and all attributes of a stereotype is not valid for all users in that stereotype, it is questionable whether this model is an adequate solution. Hend and Maia [10] have developed an AHAM-based (Adaptive Hypermedia Application Model) adaptive tutoring system. They have used IMS LIP and IEEE PAPI standards. In their research, the learner model has been classified in four parts: 1. 2.

3. 4.

Machine based: keeps data of delivery format, security, access attributes etc. Learner based: Learner selects courses or curriculum to define general purpose of learning. Sub-goals, demographic information, stereotype-data such as cognitive profile, learning preference etc. are also selected by learner. Role of teacher is to update these data System based: History of interaction, teaching portfolio, proficiencies are kept Teacher based: Teacher determines sub goals.

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In Hend and Maia’s [10] research, teachers have working load. It is obvious that tutors cannot be available all the time and teacher cannot guess learners’ cognitive style or other cognitive attributes properly. In adaptive systems, learners should access tutoring systems without support of teachers. But in this system, teacher has a role in every step of learning. There are several upper ontologies to be able to use in learner models [8] [14]. Theses ontologies may be a basis for learning model ontologies since they have general classes and relationships. Yet, they are inadequate to be used as modeling the learning process in order to meet the fundamental educational requirements. Paneva [15] proposed an ontology based on a user model that uses IMS LIP and IEEE PAPI standards for digital libraries. Paneva [15] supports in the model that twoway information shall be kept in the system. General data like personal information, goals, interests, presentation method etc. and learner choices about multimedia digital library. Research has a good point of view by relating the model with multiple intelligence framework. Yet, it has weaknesses as it uses stereotyping or putting forward the importance of model in computer context instead of cognitive convenience of the model to users. In literature, overlay model is another model mentioned frequently [5], [16], [17], [18], [19], [20]. In overlay model, user information is stored as a subset of domain model. For all sub-domains, user knowledge about this sub domain is expressed as quantitatively or qualitatively. Overlay model is criticized as it is too simple [19]. It is mentioned that user model cannot always be defined as a certain subset of an expert model. From this point of view, bug model is proposed. Basis of this model is that a user may have both right and wrong information. The purpose of the model is not only to point out the buggy information gathered from the user, but also to adapt the user by identifying wrong information that he/she has. One step further of bug model is the genetic model. In this model, user knowledge is identified from simple to complex and from special to general. Although these two models have stronger capabilities than overlay model, it is reported to be harder to apply them to tutoring systems [17]. To sum up, in practical terms, there are no systems that genetic model is used in. Use of bug models are only limited with simple problem solving related intelligent systems. On the other hand, overlay model is very popular in web based adaptive education environments and adaptive hypermedia systems. Shortcomings of the models in literature for learning object and semantic web based tutoring systems are considered and to overcome deficiencies, a learning model is needed.

3 A Learner Model for Learning Object Based Personalized Learning Environments First problem encountered in learning models is how to gather data about learners. It is mandatory to get personal information from learners by direct input. In addition, the system should guess preliminary knowledge for each user to adapt the learning environment for them. At this stage, pretests may be a solution; but, taking pre-tests as a first step might decrease learners’ motivation [7]. This situation is called as “cold start problem” and to overcome, the system should gather knowledge about learner’s academic history without disturbing him/her.

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In our learner model, cold start problem will be solved by curriculum ontologies. By use of these ontologies, system will infer learner’s current knowledge by taking the current grade of learner. Model will use both curriculum and domain ontologies to place a new learner in a proper learning location. The proposed model is constructed by answering three fundamental questions: What will be modeled about learners, how will it be modeled and how the sustainability of the model would be maintained? 3.1 What Will Be Modeled, How Will It Be Modeled? The information which will be kept about user embedded in the model is listed as follows: • • •

•

• •

Demographic information: learner related demographic information will be kept in this part. Current learner status: learner’s current knowledge about the domain will be kept in overlay model by use of ontologies. Interaction of domain ontologies and user model, current status of learner will be inferred. Expectations: The expectations (or learning goals), which learners are expected to acquire, will be provided by the curriculum ontology. In this ontology, the expectations will be kept hierarchically and be related to each other by ontological rules. Expectations and LO relationship will be provided by inferring and reasoning over curriculum. Domain ontologies are to provide the course and subject list to the learner. Individual attributes: Individual related data such as cognitive attributes, learning styles, presentation types etc. will be kept in this area. Cognitive attributes are frequently cited in the literature [2], [3], [10], [17], [19]. Yet it is not clearly stated how to keep cognitive attributes or how to correlate the cognitive attribute and the domain. In our model, cognitive attributes will be modeled by means of CogSkillNet cognitive skills ontology which is developed by Askar and Altun [21]. A new learning profile ontology will be developed and domain ontology, cognitive skills ontology and learning profiles ontology will work together to provide proper learning content to learners according to their cognitive skills and learning profiles. Performance: Completed courses, resolved tests, progress status, achieved gains and other results which emerge in the interaction of system and learner will be kept in this area. Context Attributes: Technical data such as connection speed, operating system of learner etc. will be kept in this area according to the learner’s preferences.

3.2 How the Sustainability of the Model Would Be Maintained? When users completed their study on LOs, they will be provided feedback via the system, such as the difficulty of the course, presentation strategy, relevance of the LO with learner or with subject etc. Learner model will be updated by these feedbacks and current status and progress of the learner will be updated in the system. Initial UML diagram of the user model interacting ontology classes is shown in Figure 1.

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Fig. 1. UML diagram of user model

4 Conclusion In this paper, a learner model is proposed to be applied in personalized e-learning environments. It is suggested that this model should be ontology based to provide reasoning and inference functionalities. Currently, we have developed the CogSkillNet ontology. Also CogSkillNet based LO navigation software is being implemented. This navigation system will provide ontological support to search, navigate and find LOs, and will suggest related LOs related to queries of learners. When the model is implemented it will be verified and tested in the LO navigation system. Another component of the model, learner ontology and supportive curriculum and learning profile ontologies are to be developed as future work Acknowledgments. This research is supported by TUBITAK SOBAG, with the grant number of 110K602.

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