Design modules of the framework

In this section, I address the research question – “What are the design modules of the framework?” This section is divided into three parts – the background in the

construction, the conceptual model and the system architecture of the theoretical

applications, via the relationship mappings that were found between the established Dunn and Dunn learning style model (Dunn and Dunn, 1978) and the more recent context space (Wang, 2004) and the categories of contexts (Schilit et al., 1994; Chen

and Kotz, 2000). This highlights the importance of particular learning contexts in m- learning applications, which I discuss in the background of the construction of the framework. The conceptual model describes and illustrates the three components of the framework – Learner’s Schedule/Profile, Suggestion Mechanism and Learning

Object Repository. The purpose of this model is to present an overview of my

framework. Finally, the system architecture of the framework is presented. This is divided into three layers – Learner Model layer, Recommendation layer and LOs

layer. The design and technical details of the components are illustrated.

Background in the construction of the framework

The Dunn and Dunn LS model (Dunn and Dunn, 1978) is an established model constructed over thirty years ago which targets students conducting traditional means of learning. Many factors within the components of this model were found by Yau and Joy (2006a, 2006b) to have a direct relationship mapping to the dimensions of the context space formed by Wang (2004) and the four categories of contexts defined by Schilitet al. (1994) and Chen and Kotz (2000). Recall that the context space consisted

of the identity, spatio-temporal, facility, activity, learner and community dimensions

and the four categories of contexts consisted of thecomputing, user, physicalandtime;

these were described in 2.4.2 and 2.4.1 respectively. Many design considerations, which should be taken into account when developing learning materials for m-

learning, were included within the five categories of the Dunn and Dunn model (Dunn and Dunn, 1978; Yau and Joy, 2006a, 2006b).

In view of this, I based the recent m-learning research on the Dunn and Dunn model (1978) as providing a solid theoretical foundation to the framework. The relationship mappings between the model and the context space as well as the four categories of contexts are depicted in Table 4.2. A description of the relationship mappings between the factors within each of the components of the Dunn and Dunn model (i.e. environmental, emotional, physical, sociology and personality components) against the context space as well as the categories of contexts is provided below.

Table 4.2: Relationship mappings between the Dunn & Dunn model and Contexts Dunn and Dunn LS Model Context Space and the

Categories of Contexts Environmental Noise level; Temperature; Light Physical context

Seating N/A

Layout of Room/Location User Context & Spatio- Temporal Dimension

Emotional Motivation; Degree of

Responsibility; Persistence; Need for Structure

Learner Dimension

Physiological Modality Preferences

Intake (Food and Drink) N/A

Time of Day Time Context & Spatio-

Temporal Dimension

Mobility N/A

Sociological Learning Groups; Help/Support from authoring figures; Working alone/with peers; Motivation from parent/teacher

Community Dimension

Psychological Anxious/Depressed; Somatic Complaints; Aggressive Behaviour; Attention Problems; Delinquent Behaviour; Social Problems

Learner Dimension

The Environmental component– Many of the factors within this component

This component specifies that learners may have preferences to study in different locations and under different noise levels, as indicated by the location and noise level factors. When learners are performing m-learning, the learning impact may be particularly affected by the location of where the learning is taking place, for example, whether in a classroom, on a train/bus, or in a restaurant. The level of noise in the learning environment may also affect the student's concentration. Hence, the preferences of learners to study in different locations and under different noise levels should be taken into consideration when developing an m-learning application.

The Emotional component – The factors within this component can be

mapped onto the learner dimension. This component specified that learners have

varying levels of motivation and degrees of responsibility to carry out their learning. Similarly, m-learning often involves learning on one’s own and may require a lot of motivation and a certain degree of responsibility. Hence, it is preferable that the learners’ level of motivation and degree of responsibility are taken into account when developing an m-learning application.

The Physiological component – Some of the factors within this component

can be mapped onto the learnerand spatio-temporal dimensionand thetime context.

This component specifies that learners may have different modality preferences (i.e. visual, auditory, kinaesthetic/tactile learning), and their performance in learning/studying may be dependent on their intake (food and drink), the time of day and how mobile they felt (mobility). In terms of e-learning or m-learning, there is evidence to suggest that, whilst using instructional technologies to learn material, a student’s performance can be affected by their preferred LS, and visual learners are positively affected (Hall and Pittman, 2005). Kinaesthetic learners may also prefer to learn in the situational context.

Concentration levels of students may be different depending on whether the study period was before, during or after intake of food and drink. The time of day can determine the location, which can affect learning/studying. For example, a learner may not be willing to learn/study in the bedroom when getting up in the morning or in a restaurant after an evening meal. Also, learners may have preferences for learning during different times of the day. Some students may prefer learning whilst they are on the move, whereas others prefer to learn/study in fixed locations. Hence, it may be preferable to consider these factors - modality preferences, intake, time of day and

mobility– when developing an m-learning application.

The Sociological component – the factors within this component can be

mapped onto the community dimension. This component specifies that learners may

have preferences to study in a learning group and/or working alone or together with peers. Hence, these factors should be considered when developing possibly either in an independent or a collaborative m-learning application.

The Psychological component – the factors within this component can be

mapped onto the learner dimension. This component specifies that learners may have

varying levels of attention. Whilst learners are performing m-learning, their attention may be affected more easily because there are possibly elements of increased noise, movement, interruptions and distractions. Therefore, this should also be considered in the development of an m-learning application.

The importance on the consideration of a number of factors was described in the background in the construction of the framework. These include the following.

 The location of study and noise level (environmental component).

 Motivation and degree of responsibility of a learner (emotional component).  LS, food and drink, time of day and mobility (physiological component).

 Independent/collaborative learning (sociological component).  Attention level (psychological component).

These factors are potentially important learning contexts that should be considered within m-learning applications. At this point in the thesis, I consider only the location of study and LS contexts (from the above list of factors) to be incorporated into my framework. As mentioned in 4.3, noise level is not considered. I consider food and drink, time of day and mobility to have less relevance relating to the recommendation of materials than the ones I have chosen to be incorporated. I target independent learners in the framework, therefore collaborative learning is not considered. Motivation, degree of responsibility and attention level may be incorporated into the framework, as part of the future work. It was, however, decided in chapters 5 and 6 that the motivation of a learner is critically important and has a strong positive correlation with the concentration of the learner. Therefore, my refined framework, described in chapter 6, uses the motivation level context in place of the concentration level of the learner.

Conceptual model of the framework

The conceptual model of my framework consists of three components – Learner’s

Schedule/Profile, Suggestion Mechanism, and Learning Object Repository, as

illustrated in Figure 4.1. The learner’s learning contexts (i.e. the available time and the type of location) are captured via the user’s learner schedule. The learner profile (i.e. the LS and knowledge level) is input into the device by users. The suggestion mechanism is expected to suggest appropriate LOs to students based on their learner profile and contexts.

The initial scope of learning materials to be made available to students through the framework is the Java programming language, in the form of LOs. The initial target of students to use the end application includes undergraduate computer science students (i.e. typically novice programmers). The reasons for the decision to incorporate these materials were that usually a large amount of time and motivation are necessary to learn an object-oriented programming language such as Java. The three components – Learner’s Schedule/Profile, Learning Object Repository and the

Suggestion Mechanism– are described in detail below.

Figure 4.1: Conceptual model of the mCALS framework

1. Learner’s Schedule/Profile– Via the Learner’s Schedule, the learner supplies

to the system their daily study-related and -unrelated events. A unique identifier, event start and finish time, geographical location, type of location and event type are to be recorded. Via the Learner’s Profile, personal information about the learner is recorded, including a unique identifier for the

Appropriate learning object(s) suggested to students based on their Learning Contexts and

Learner Profile Learning Contexts- Available Time, Type of Location Learner Profile- LS, Knowledge Level Suggestion Mechanism Learner’s Schedule/ Profile Learning Object Repository

learner, surname, forename, gender, date of birth, degree and modules undertaking and their preferred LS according to the Felder and Silverman model (1988), i.e. each of the learner’s preferences under the following categories are recorded – (a) active/reflective, (b) sensing/intuitive, (c) visual/verbal and (d) sequential/global. Their knowledge level relating to the Java programming language is also ascertained by performing a simple test. The above-mentioned information is stored in the Learner’s Schedule/Profile. The use of a learner profile is important during m-learning because different types of users may require m-learning devices for different reasons and may require different capabilities of the devices (Parsonset al., 2006). For example,

a music student may require audio capabilities whereas an art student may require drawing capabilities from the device.

2. Learning Object Repository – all LOs are stored in this database. Different

types of LOs are stored including compulsory activities (such as assessments), non-compulsory activities (such as exercises) and revision activities (such as reviews). Each LO has the following attribute – a unique identifier, title, subject, description, activity objective, priority of activity to be undertaken (high, medium, and low), duration of time needed for completion and status of activity (unfinished or finished). If the activity is not finished then the remaining duration of the activity is recorded. The LOs for Java consist of factual information, examples and multiple choice exercises and tests. Different types of LOs to facilitate learners with different LS based on the Felder and Silverman model (1988) can be made available and incorporated into the database for possible selection to students.

profile and learning contexts suggestion. The learner profile suggestion has

two functions - to select appropriate materials to students based on a) their LS, and b) their knowledge level. The LS and the knowledge level of a student are taken from the Learner Profile as input, and appropriate LOs are selected and then are output to the learning context suggestion mechanism. The learning context mechanism then takes the values of the learning contexts – type of location and available time of the student – together with the filtered LOs according to the learner profile suggestion, to further select LOs that are appropriate to students in those contexts. Location information is later converted to information relating to the possible concentration level of the learner and frequency of interruption at that location, described in the next section.

System architecture of the framework

The system architecture illustrates the design and technical details of the components within the framework. It is logically divided into three layers - Learner Model layer,

Recommendation layer and LOs layer, as illustrated in Figure 4.2. Each of the layers

Recommendation Layer

LOs Layer

The Learner Model layer consists of four system components – Learner

Profile, Learner Schedule, Update_Knowledge_Level and Student Database. A

graphical-based calendar is displayed for ease of entry for users to enter their scheduled events (including nature of event, location, time start and finish), which are stored in the Student Database. For the purpose of retrieving and transferring the event details with ease to other system components, calendar events are transformed into ICS format, described in 9.1. LS, knowledge level, user ID and name are input into the Learner Profile, which is stored in the Student Database in text format.

In the Recommendation layer, I use the location attribute to calculate two default values for the level of concentration and frequency of interruption typical for that type of location. The values of these attributes in relation to the location were obtained by a study performed by Cui and Bull (2005), where they found that

ICS Format Text Format Retrieve_Contextual_Info RetrieveLocationAttributes() RetrieveAvailableTime() UserConfirmsContextualAttrib utes() UpdateContextualInfo() Learner Schedule Nature of Event Location Time Start Time Finish Suggestion Mechanism Recommend LO() Update_Knowledge_Level StoreCompletedLOs () StorePendingLOs() DisplayCompletedLOs() Learner Profile LS KnowledgeLevel UserID Name Learning Object Repository Student Database

Figure 4.2: System Architecture of the mCALS Framework

LO LO Learner Model Layer

different students had the same perceived level of concentration as well as frequency of interruption in the same location, although the noise levels may have been different. I propose to use these findings as default levels of the student’s concentration level and frequency of interruption at the location, in place of the type of location context.

Using the Time Start and Time Finish attributes, the available time that a student has at a particular point in time can be obtained.

The Retrieve_Contextual_Info component first retrieves the learning contexts

information (location and available time) from the Learner Schedule and then transfers these into actual approximate values which can be used by the suggestion mechanism. The attributes taken from the Learner Schedule include Location, Time Start and Time Finish. A method is put in place to give the user the option to view and confirm the values of these attributes, or change these values, if necessary. The method is used to update this contextual information, as necessary. The parameters fed into the Suggestion Mechanism includeLS, knowledge level, concentration level,

frequency of interruption and available time. The Suggestion Mechanism then uses

this context values to suggest appropriate LOs to learners.

In the Learning Object Layer – the LOs that have been recommended to students are stored along with the following information – whether the student has completed the task, in the case of a test or exercise, and whether the student has completed it correctly. This information is transferred to the student database and when the student has attempted an appropriate amount of material accurately, their knowledge level is increased. Three methods are used to support the updating of a student’s knowledge level – store completed LOs, store pending LOs and display