based on their interaction with system. To achieve this, we apply techniques such as machine learning algorithms. Once a learner model is created, reasoning from that model can be made possible by drawing several assumptions about the learner’s current situation.
Machine learning techniques are commonly used for learner modelling because of the complex nature of relationships between learner contexts, which are difficult to rep- resent. The process is based on storing and exploiting information about the learner. However, learners differ in traits such as skills, aptitudes and preferences for process- ing information, constructing meaning from information, and applying it to real-world situations [51]. This is an acknowledged problem for Web-based education, with Aiken and Epstein [5] stating that learner models should acknowledge that learners might have different learning styles and skills.
Neural Networks [146] [163], k-Nearest Neighbors [140], Bayesian Network [158] [103] [143] and Fuzzy [10] algorithms are some of the techniques that have been used to model learner contexts. There is a great need to identify the interests/characteristics of the learner on the delivered learning content by modelling and understanding the
learners’ actions and needs. Most researchers agree that learner modelling is the
most important part of any adaptable learning system with many challenges. Some approaches based on machine learning techniques are provided Section 2.7.
5.3
Workflow of Personalisation System
In this section, we describe the workflow of the personalisation system (see Figure 5.1), which can be summarised in the following steps:
Data Collection:
Initiation of a learning activity begins when the learner carries out the learning activity by interacting with the m-learning application through the selection of some
5.3. Workflow of Personalisation System 79 I n p u ts Data Classification Shared Personal Preferences characteristics Time Environment Device Hardware Software Educational Content Learner Profile Representation
The most effective contexts after filtering
and analyzing .
Data Collection
Forms, Login, Feedback, Sensors, Educational Content Request
Learner Model Creation Learner Model Status
Situation Status Educational Status Knowledge & Shared Properties
Update O u tp u ts Interface Reasoning Engine
Figure 5.1: Workflow of the Personalisation System.
actions presented on the learner’s device interface. If using the application for the first time, the learner will be asked to complete some forms to record personal information (e.g. name, age and job). Other information will be gathered by alternate means (e.g. sensors). Identity verification is essential; the system must first be able to identify the learner in order to collect the required information to perform the adaptation based on the learner request. This will result in improved accuracy and consistency.
There are numerous available techniques to gather all related information about learners and the methods/algorithms used to process such information to create learner profiles and provide adapted content [63]. To construct a learner model, all related information about the learner such as (hardware, software, capabilities, preferences of the learner, etc.) should be gathered and transferred to the learner model. Both Implicit and Explicit information is collected and recorded in the learner model. The methods of gathering learner related data can be categorised into five main sections:
5.3. Workflow of Personalisation System 80
• Forms: Direct questions to the learner are the initial information needed to construct the learner model. This method is an effective way to gather general information about the learner such as demographic data, interests, preferences, etc. Set of questions are used mainly for collecting contextual and personal information about the learner. This information will be used by the reasoning engine. While the information provides the ability to further personalise the experience for the user, an excessive number of questions is likely to disturb the learner [18] [139]. Existing adaptive systems commonly use self report and questionnaires to obtain initial information [91] [136].
• Login: In order to provide personalisation for individual learners, the system must first be able to identify the learner in order to collect the information required to perform the personalisation.
• Feedback : This includes the information obtained through interactions between the learner and the system, including the learner history page visits, access length and frequency, and outcomes given by the system. However, information gathered in this manner may not be completely reliable [166]. The system is able to develop a concept of the attitude of the learner through the interaction with the system, i.e. observing learners’ actions and behaviours (e.g. the learner will be able to tell the system whether they like or dislike the adapted content using a like/dislike response). The like/dislike responses will have a significant impact on the structure of the learner profile, with feedback from the learner used to inform the changing learner profile, and hence to alter the interaction between the system and the learner.
• Information from sensors: A range of data can be collected from sensors, such as the learner’s position from in-built GPS, time from the in-built clock, and
5.3. Workflow of Personalisation System 81
noise from the in-built microphone.
• Assumption: In some situations, more information about the learner is needed but cannot be obtained through the direct questions or sensors. For example, the learner background knowledge on the subject is unknown for the system. In such situations, the system makes an assumption at the beginning that the learner has no background knowledge. The system must offer a help option for inexperienced learners when they use the application for the first time [85]. The data obtained using the above methods should be transferred to variables and stored in a learner model. Variables stored in a learner model can have three forms: boolean, discrete, and continuous.
The system is able to offer a learning activity, and the learner will decide whether to accept or reject the learning activity. If the learner chooses not to carry out the learning activity, there is no further process. If the learner is willing to participate and wants to begin the activity, the system will issue a start up application to indicate that the learner wants to undertake a learning activity.
Data Classification:
Learner data is gathered and categorised into several class types as shown in Figure 5.1. It consists of:
• Personal Context - all attributes relevant to the learner throughout his/her use of the system; and
• Shared Context - attributes relevant to all learners when using the system. Learner Model Creation:
The learner model aims to make information systems learner-friendly by adapting the behaviour of the system to the needs of the individual. The structure of the learner model will be discussed in Section 5.4. When the learning activity is in progress or