Modeling Learner Profile with respect to Cloud eLearning

The existing IMS LIP specification standard is used as a basic model for modeling the CeL Learner profile (Ce2LP) characteristics, with a considerable extra number of features.

In order to satisfy the aim of CeL[140], IMS LIP (containing:(1) identification (2) affiliation, (3) relationship, (4) accessibility, (5) competency, (6) interest, (7) activity, (8) qcl, (9) goal, (10) transcript, (11) securitykey ) is enriched with additional seven elements which contributed to model the Cloud eLearning learner Profile (Ce2LP) with respect to the CeL demands and continual changes overtime. Such elements as: Cognitive/Learning style, knowledge level, knowledge gained, short and long interests, social preferences and emotional state, described further in Table 6.2.

Table 6.2 CeL Learner Model Extra Attributes

CeL Description

1.Cognitive/Learning style The learning style of the learner

2. Knowledge Level The knowledge level in particular topics/courses 3. Knowledge Gained The knowledge gained (positive test result) in CeL

4. Short Interests Interests which are shown in short terms (example: a topic that is visited in short term and never looked back again) 5. Long Interests

Interests (topics) that are studied continuously and, longitudinally and frequently visited (studied once and continually coming back to the same course/topic) 6. Social Preferences Information related social activities (relevant information

, example learners’ opinions, feelings, likes etc.) 7. Emotional State

Information about emotional state, if the learner is confused, bored or even if the user is in the mood of learning simpler or more complex tasks at some particular moment

In this regard, the Cloud eLearning Learner Profile - Ce2L:

Definition 6.3: Ce2LP defined as metadata for Cloud eLearning Learner Profile is used to model and represent the learners of Cloud eLearning.

Further, the Cloud eLearning Learners profile (Ce2LP) is encoded using XML, as shown in Figure 6.8.

Fig. 6.8 An example of the knowledge level elements of Ce2LP

The features served for modeling Ce2LP are acquired in different phases, by explicitly asking the learners to complete the registration information, through questionnaires, or

6.4 Modeling Learner for Cloud eLearning 95 through implicit manner, by monitoring learners’ activities which is updated over time. For example, chunking all the elements of a particular user, the following data are derived either directly or indirectly:

• Directly: The personal data required from the user, such as:

– name, birthday, address, gender, background knowledge, preferences, experience, domain of interest, role, username, password, etc.

• Indirectly: the data acquired from monitoring learner performance, such as:

– learning style, knowledge gained, test results, rating of an item, item studied, click through, item visited, favourite, previous units, current units, progress achieved, overall time spent in the system, overall time spent in a unit, etc.

All aforementioned elements are represented using a combination of learners modeling methods explained above in section 6.5. The adaption of the learning environment using Learning styles has been implemented and evaluated in various systems. In this thesis the learning styles are represented using the stereotype model, by dividing the learners using the linear set of categories such as:

(i) Visual, (ii) Auditory,

(iii) Tactile and kinesthetic learners.

Where the visual learners tend to learn more through visual approaches (for example: through videos), the auditory learner prefer the learning while listening, they often may read loud and listen to themselves. Furthermore, the tactile (touch) and kinesthetic (movement) learners prefer the involvement and memorizing of the learning through the interaction of objects1[141]. With respect to Cloud eLearning, as explained in section 6.5. the modeling of the learners’ uses the hybrid approach, where stereotype learner model is used to represent the learners’ knowledge level categorized as novice, advanced beginner, competent, proficient, and experts. The background of the learners’ is used while following the overlay model, where the topics are saved to express the learners’ progress as elements of knowledge domain. The cognitive theory is used to acquire learners’ learning style. Above that, the learners’ short-term and long-term interests are acquired using Text Mining technique, by modeling the vector of concepts extracted from searches and visited relevant sources and storing them as part of the learners’ interests, shown in Figure 6.9.

Fig. 6.9 Text mining technique for storing relevant concepts for user short-long interests

As shown in the process flow in Figure 6.9, the user’s explicit searches, their browsing activities (example in social media and their overall browsing activities) together with the textual content expressed through direct queries are captured in a log file. The raw data stored in the log file are used for further processing throughout the text mining phase. In the very beginning the raw information are cleaned and tokenized. During the cleaning and tokenizing process, the html tags are removed and the sentences are chunked in words, so that semantically the similar words are matched together and indexed to the same indexing term. Further, the remaining data are processed under “stop word process”, resulting with the removal of a list of stop words, such as: “the”, “a”, “and”, “an” etc., which have the highest usage frequency overall. Then, the stemming process maps all inflectional forms of words to the same root form. For example, words computer, computation and computing are all derived from “compute”, and only the root word, in this case the “compute” word is stored for the indexing phase. In [142], is presented the how the stemming algorithms is used to reduce the number of a word by mapping the nouns, adjective, verb, adverb etc. to its root word. The Portman stemming algorithm used in our approach is considered as the most usable algorithm which produced the most suitable output compared to other existing algorithms [142]. The final concepts are indexed and scored, where the top 5 stored concepts that are relevant

6.4 Modeling Learner for Cloud eLearning 97 within the particular knowledge domain are stored in the learners’ respective interests. These knowledges gained, are listed as topics/subtopics that the learner has completed after the CeL has proposed to the user.

Summary

This chapter reviews the contributions that have been made so far in order to provide knowledge representation. In this chapter a various number of theories and techniques have been analysed through which it has been clarified how the knowledge and learners’ profiles are modelled so far. By reviewing existing systems and standards, we conclude that the new type of representing data and learners should be followed, namely Cloud eLearning meta-data and Ce2LP respectively which incorporates a set of elements (Table 6.1) which are missing in the existing standards. So, Cloud eLearning meta-data is based on IEEE and Dublin core standards and furthermore it has five additional elements described in Table 6.1, whereas the Ce2LP is based on IMS LIP specification standard and additionally adds seven extra features shown in Table 6.2. From one side, the Cloud eLearning meta-data facilitates the process of integrating the Learning Objects to Cloud eLearning Learning Objects (CeLLOs) through the transformation process depicted in Figure 6.7. From the other side, the CeL Learner profile (Ce2LP) with the seven extra elements models a dynamic learner with a number of characteristics explained in Table 6.2. Both approaches, CeLMD and Ce2LP facilitate the process of generating automated personalized learning path. However, since we are dealing with huge numbers of CeLLOs as part of CeL, the artificial intelligence planner will not be able to cope with such searching space, therefore we are obliged to implement a threshold filtering through recommender systems explained in the next chapter.

Recommender Systems

Even if the learning objects are represented in the proposed standard form discussed previ- ously, it is obvious that the Learning Cloud is a huge space to search in order to find those CeLLOs that should be presented to the learner arranged in a sequence of personalized learning path. In addition, combinatorial explosion creates an inevitable computational problem in any automated process, such as planning, that attempts to construct such paths. The main issue in classical techniques of artificial intelligence automated planning is the experience of exhaustiveness when dealing with the huge number of nodes in the search space (searching for numbers of objects as part of the search space). So, in this regard it is important that the pool of appropriate Cloud eLearning learning objects is relatively small so that we avoid combinatorial explosion (which tends to have many actions and states) during planning, which is the main limitation factor when trying to generate a solution from one engine in a single run [143].

So in this context, the Cloud eLearning recommender system (hereafter CeLRS) is involved between the knowledge representation and AI automated planning, which helps us to reduce the search space as well as to prioritise the Cloud eLearning Learning Objects that would be in the final learning path. We intend to introduce the basis of recommender systems and its related technologies which are being used in order to create a successful recommender system which filters a list of CeLLOs that matches with the learners’ profile.

So, in this chapter recommender technology is described and we propose the Cloud eLearning Recommender System as a middle-layer of the overall Cloud eLearning architec- ture, in order to filter the most appropriate Cloud eLearning Learning Objects for a particular learner background and instant desire. The hybrid approach for building the Cloud eLearning Recommender System is elaborated, in order to rank the relativeness of learning objects through content filtering and the prediction of the learning objects through collaborative

7.1 The Basis of Recommender Systems 99