Context-aware recommender for mobile learners

The Semantic Web is an extension of the current web, whereby content is given well defined meaning so that it can be understood and processed by both software agents and humans [1]. Semantic Web provides an infrastructure with the potential to efficiently reason with content that is defined at the semantic level with formalized knowledge [2]–[6]. The challenge in an information-rich world is not only to make information available to people at any time, any place, and in any form, but also to offer the right content to the right person in the right way [7]–[9]. In particular, in the context of mobile learning, if such approach is to be adopted, many research efforts are needed to close the gap between learning strategies, knowledge discovery, and context perception and management on the Semantic Web [10]. These efforts would lead to a Semantic Web that has the potential to revolutionize the way learning services available on the web are discovered, adapted, and delivered to mobile users based on their context. To achieve this goal, we need to formally describe not only web-content, but also the various system stakeholders including content producers, content consumers, and surrounding context. This approach is adopted in this paper by integrating learner’s knowledge, domain knowledge, and context knowledge. At the semantic level, meta-information and reasoning mechanisms are used to integrate these knowledge components, thus making it possible to infer the learning sequence that suits learner’s profile and experience, and the learning content that suits their activity, used technology and surrounding environment.

In this paper we deal with learning in a mobile environment. Although, much progress has been made in the field of mobile services [11]–[15], we believe that more research is needed in the area of mobile learning. This is mainly due to the fact that most existing systems have been designed with focus on technology only [16], while they should equally address knowledge representation and learning strategies’ aspects for mobile learning. In particular, several obstacles still hinder personalization of mobile learning services, such as: (i) current mobile learning services act as passive components rather than active components that can be embedded with context awareness mechanisms; (ii) existing approaches for service discovery neglect contextual information on surrounding environment; and (iii) lack of context modeling and reasoning techniques that allow integration of various contextual features for better personalization. In this paper, an attempt is made to solve some of the above mentioned problems, aiming to build a mobile recommender system with semantic-rich awareness information. Our goal is to develop services capable of providing content recommendations tailored to the learner’s background, context, and task at hand. Mobile learners expect to access such learning services from various ubiquitous setups (workplace, home, on the move, etc.) with different operational characteristics such as varying network bandwidth and limited resources on mobile devices [17]. Also, it is important to note that the operational environment of a mobile setting changes frequently. It is therefore necessary to consider a proactive context awareness mechanism that can sense both system-centric and learner-centric context and adapts the accessed services accordingly at run-time. In such setup, context aggregation can be made possible using a shared ontology space and a unified reasoning mechanism. In particular, whenever context change occurs, the system identifies the new contextual features and translates them into new adaptation constraints in the operating environment. The contribution of this paper can be summarized into three points. First, a shared ontology space for capturing, integrating and modeling contextual knowledge at a higher level based on learner context, activity context, device context and environment context. Second, a method for structuring semantic knowledge about subject-domain in such a way to enable provision of content at different granularity levels to suit learners with various background and skills. Finally, a model for run-time context management that translates context changes into system-centric and learner-centric adaptations for better personalization.

The remainder of this paper is organized as follows. Section 2 reviews related work and discusses the main challenges for developing context-aware mobile learning systems. The overall system design and architecture, including context modeling are presented in section 3. Section 4 discusses the reasoning mechanisms used to deal with context integration and management. The experimental results are discussed in section 5, and finally, conclusions are drawn and further research work is suggested.

Research in the field of personalized learning has been dominated by the use of ontologies and related Semantic Web technologies [18]–[28]. Ontology is a representation of a set of concepts within a domain and the relationships between those concepts [18]. It is used to reason about the properties of that domain. Ontology concepts are defined in terms of classes which are extended with properties. These are commonly encoded using ontology languages such as the Web Ontology Language (OWL). In particular, ontology-based approaches have been widely used for context modeling and management [8]–[12]. A variety of ontologies have been adopted to model knowledge about subject domains, users, resources, and other contextual elements of the user surroundings [8],[29]–[34]. In such setups, reasoning techniques are usually applied on metadata derived from a single ontology. However, real-life learning systems require simultaneous use of knowledge derived from multiple ontologies. Cross-ontology reasoning presents many challenges. One major challenge is related to disparity and semantic mismatching between related context elements across ontologies [35],[36]. A solution to this problem is efficient integration of various ontologies into an upper ontology space capable of capturing and modeling information related to the global shared knowledge at a higher semantic level, and thus simplifying cross-ontology reasoning. Another problem of concern with ontology based approaches is their inappropriateness to reasoning with uncertainty. It should be noted that not all context elements are perceived with precision. Some of the context elements are quantized with uncertainty leading to certain ambiguity while defining and reasoning with context, [37]. This problem can be dealt with by integrating various reasoning models that may combine probabilistic, rule-based and logic reasoning techniques [38]–[40].

In addition to the above-mentioned problems, research in mobile learning recommenders has its own challenges. The task of a mobile learning recommender can be considered, to a certain extent, as a knowledge management task to support the learner’s current activity. This is because the goal for mobile learning is not always long term learning, but, in many cases, an on-demand learning process triggered by immediate real-world needs. In such learning environment, the context of the learner matters, and the system should be able to capture the learner’s profile, learner’s context, task at hand, and the environment in which learning occurs. This knowledge is important to provide context-aware delivery methods that can generate content that meets the immediate learning goals. Other challenges that need to be addressed to develop robust mobile learning recommenders are those related to (i) the limited-resource mobile technology; (ii) low-bandwidth and unsecured wireless communication; (iii) heterogeneous context management and (iv) personalized content retrieval. This paper attempts to solve some of the problems related to the last two challenges, heterogeneous context management and personalized content retrieval, making use of the progress made in ubiquitous computing and the Semantic Web respectively. Before describing our system, we first overview recently published work in the field of ontology-based content retrieval and adaptation for e/m-learning. We then particularly focus on work in m-learning recommenders.

Dolog and Nejdl [33] have used knowledge extracted from four different ontologies (subject, user, resource, and link ontology) to realize personalized access to resources on the Semantic Web. Subject and resource ontologies are used to provide explicit semantic about information resources and the way they fit to user queries and goals. However, user and link ontologies are used to provide additional means for deciding which links to show, hide, and annotate according to the learner’s background. The focus of their work is mainly around learner-centric adaptations. Another system that relies on learner modeling is the European Union project, Learning In Process (LIP) [41]. LIP aims to provide immediate learning content on demand for knowledge intensive organizations through incorporating context into the design of learning systems. A matching procedure is presented to suggest personalized learning content based on user’s current competency gap. The matching process tries to find relevant learning objects for a given user context by computing a similarity measure between the current user context abstraction and the ontological metadata of each learning object.

In the field of mobile learning, many approaches were adopted to develop context-aware learning services. These vary from simple systems relying solely on device content adaptation [42], to complex infrastructures for generating context aware mobile learning applications [43]. Yarandi et al.[44] have developed a mobile learning system with four modules to enable courseware management, course content adaptation, test evaluation and course recommendation mediation. The system is used to learn new natural languages. The course recommendation mediator selects suitable learning material by traversing the courseware knowledgebase according to pedagogical rules and ontological user profiles. The recommendation mediator is guided using results provided by the course-test evaluation module. Course material consists of learning objects with various granularity and their associated learning activities (writing, reading, speaking and listening). Each activity retains a learning objective, a difficulty-level, a suggested time for completion, and is associated a test score based on a probabilistic model. In a similar study, Benlamri et al. [45] have developed a combined model based on probabilistic learning techniques and ontologies to enable context processing and management in a mobile learning environment. The system uses Naïve Bayesian classifiers to deal with context elements that are quantized with uncertainty. Naïve Bayesian classifiers are used to recognize high level contexts in terms of their constituent atomic context elements involving uncertainty. Recognized contexts are then interpreted as triggers of actions yielding web service discovery and adaptation in order to achieve personalized instruction that best match the learner’s needs and the operating environment. Mobiglam [46] is another mobile learning framework that is based on Bayesian techniques. Unlike the systems mentioned above, Mobiglam allows users to access resources and other learning services through Virtual Learning Environments (VLEs) such as Moodle and WebCT. Device adaptations are provided by a J2ME application installed on the client devices, while adaptation of VLE content is done on the server side through a decision engine relying on a Bayesian learning algorithm.

Unlike the systems described above, Mobilearn [43] is a complete infrastructure developed by a consortium of European universities in collaboration with MIT and Stanford to build mobile learning applications. Mobilearn is based on the Open Mobile-access Abstract Framework (OMAF). The latter is built upon two technologies – an extended version of the MIT-OKI (Open Knowledge Initiative) which provides an open and extensible architecture for learning technology [47] – and the IMS-ALF (Abstract Learning Framework) which provides an abstract representation of interoperable services and their interfaces [48]. The goal is to build a framework that can provide various specifications (i.e. various mobile applications) based on the used services, according to the open architecture approach.

Another challenge of mobile learning is its human computer interface (Mobile HCI) aspect, and especially the provision of context-aware communication and interaction. For instance, a mobile device that support a number of network adaptors, and equipped with some sensors such a GPS (Global Positioning System) unit, a network bandwidth sensor, and a smart card for user-authentication, can provide useful context-aware functionalities that allow one to adapt the behavior of the system according to the sensed wireless network, position in space (location-awareness), unexpected network interruptions, and security threats respectively. Such system becomes able to select what content to show and how to show it based on the learner’s activity and surrounding environment. An example system with such capability is the MObile PErsonal Trainer, MOPET [49]. The latter monitors users’ positions and other physiological parameters in outdoor sports activities to present functionalities such as location-aware maps augmented with visualizations of users’ performance, or context-aware fitness advice and 3D demonstrations of exercises. Mobile learning systems should exhibit these capabilities to achieve efficient context-aware communication and interaction.

Although our approach builds on many of the techniques described above, it differs from previous work in several aspects. First, unlike the above mentioned approaches, the proposed system tries to build a global context view out of a large set of heterogeneous contextual information, including knowledge about the learner, learning domain, learning activity, used technology, and surrounding environment. Second, unlike the systems described in [33],[41],[44] which use separate ontologies for reasoning with domain knowledge and context knowledge respectively, our approach uses a shared upper ontology space that permits reasoning with the entire system-centric and learner-centric contexts, thus enabling efficient context integration and adaptation. Finally, an important aspect of our approach is the ability for reasoning under uncertainty. It should be noted that not all contextual information around the learner can be quantized with certainty, yet the semantic web models are logic based with facts either being true or false. One possible solution to this problem is to simplify contextual data, which is fundamentally continuous or uncertain, down to symbolic discrete assertions. However, this process may introduce errors which can further propagate globally when repeatedly used in the reasoning process. To deal with this issue we opted for uncertainty representations like fuzzy logic which uses non-linear combination functions. Fuzzy logic uses min/max to combine values, so repeat combination of the same information does not introduce significant errors [50] in the reasoning process.

Understanding the importance and the role of tacit knowledge is key element to formulating knowledge management strategies for mobile-learning systems. Unlike explicit knowledge that is coded knowledge, tacit knowledge is knowledge that is embedded in system actors (i.e. learners and other dynamic context elements) [56] and which is difficult to be shared and distributed. In this study, the upper ontology space, built out of the five integrated sub-ontologies is used in such a way to allow tacit knowledge related to learners and their context to be defined at the semantic level and used to a great extent in the personalization process. In particular, knowledge in the upper ontology space is structured in such a way to allow building a conceptual learner model out of a sequence of learning activities by linking a user’s conceptualization to particular subject domain ontology. This constitutes the key design aspect of our upper ontology space to enable personalized learning. A learning activity is characterized by user interactions, through which contextual information related to the user’s surroundings, as well as concepts covered, queries performed, and learning resources consumed are stored and used as knowledge facts to enable further inferences aiming to achieve better adaptation. Thus, the perceived context and the accessed domain information are efficiently used for building a personalized learning path that is aware of the learner’s interaction history, preferences, background knowledge, and operating environment. The used approach models the learner at the semantic level by providing a formal learner’s conceptualization defined in OWL, and thus allows reasoning upon it in order to infer the learner’s understanding of the subject domain. The reasoning is performed in terms of SWRL (Semantic Web Rule Language) rules that are applied on knowledge represented in the OWL-DL (Description Logics) ontology. It should be noted here that reasoning in systems integrating DL ontologies and rules is a very hard task [56]. This is mainly due to undecidability of reasoning in such systems. This is particularly the case for those systems integrating DL ontologies with recursive or hybrid rules [57],[58]. So, bridging the discrepancy in these two knowledge representations is a challenging problem. Many studies [57]–[60] have shown that to avoid undecidability of reasoning, practically all decidable approaches to integrating ontologies and rules should impose specific conditions which restrict the interaction between the rules and the ontology. In this work, we adopt a similar decidable reasoning approach by adhering to restrictions, such as avoiding recursive and hybrid rules. In particular, we use the SWRL-Jess Bridge, a bridge that provides the infrastructure for incorporating the Jess rule engine into Protégé-OWL to execute SWRL rules. The system also relies on some SWRL-built-in libraries, which include an implementation of the core SWRL built-ins, as well as mathematical built-ins, to support the use of complex expressions in rules, reasoning with temporal information and querying OLW ontologies.

The sequence of steps given below illustrates the personalization process adopted by our system in a typical learning scenario where a learner submits a query in a specific subject domain area and receives a planned learning sequence fulfilling the learning goal. This learning scenario is also depicted graphically in Figure 10.

1. 1.

   When the learner logs in, his background, preferences, and previous learning activity are retrieved.

    
2. 2.

   The learner uses the domain ontology vocabulary to query the system.

    
3. 3.

   The subject-domain ontology related to the learner’s query is identified and retrieved.

    
4. 4.

   Based on the learner’s query, the system infers the related ontology concept(s) and identifies those concepts that are part of prerequisite knowledge, core-knowledge, and related knowledge using HasPrerequisite, HasNecessaryPart, and HasPart properties respectively.

    
5. 5.

   Next, the system uses the perceived device and environment atomic context elements to infer metadata that adapts the search for those learning resources that are suitable for the system-centric context.

    
6. 6.

   The metadata generated in (4) dealing with related domain ontology concepts, and the system-centric metadata generated in (5) are then used to discover and filter out learning resources from various learning repositories.

    
7. 7.

   The system will then determine the learner’s expertise in the subject-domain (i.e. tacit knowledge) by inferring previous learning activities, covered concepts, adopted learning paths, and consumed learning resources. This knowledge is used to build a personalized learning sequence that is aware of the learner’s history and available learning time. Thus, the newly constructed learning sequence consists of optimized system-centric learning resources to fulfill the current learner’s activity and goals.

    
8. 8.

   The personalized learning sequence is then provided to the learner for navigation.

    
9. 9.

   Based on the newly selected concept, learner’s expertise is automatically updated and the personalized learning path is re-adjusted by resuming processing from step (4).

    
10. 10.

    The learning activity terminates when either the learner logs out the system, or when all domain concepts related to current activity have been covered.

     

The above strategy strives to meet the best personalized learning path by dynamically updating the learning sequence based on the learner interactions with the system and surrounding environment. Below, we give a detailed description of the various system-centric adaptations and learner-centric adaptations used in the personalization process. We also provide the algorithm used to help navigate a learning path.

4.1 System-centric adaptation

In a mobile environment it is important for the system to consider system-centric adaptations to cope with the lack of resources in used devices and the unsecured low-bandwidth wireless network. Thus, system-centric adaptations aim at filtering out those learning resources that can efficiently be transmitted over the network and properly run on used devices. These adaptations are triggered by the context monitoring process which identifies context changes and proactively performs actions such as restricting media type and pruning large learning resources from the learning path in case of low network bandwidth. A number of inference rules have been developed to operate on perceived device and environment atomic context elements to achieve this goal. The diagram shown in Figure 11 describes the logical steps used to achieve the main system-centric adaptations considered in this study.

When the learner logs into the system, first, the system senses the used network adaptor and retrieves its connection speed. Connection speed is an attribute that is straightforward to obtain and it represents the maximum theoretical speed for the used wireless adapter [56]. Knowing the type of the network connection, such as IEEE 802.11 or GPRS, gives our reasoning engine the insight that allows it to make some adaptation choices related to media-type and size of resources that are to be retrieved. This is achieved by taking into account the available bandwidth and device features. For example, if the network connection is IEEE 802.11, the system does not need to sense the network bandwidth and does not need to make any restrictions on media type because the available bandwidth is stable and large enough to handle all type of resources. However, if the sensed connection is GPRS, the system adapts the media-type and size based on the available bandwidth as explained below. For example, for a GPRS connection with a maximum connection speed of 48 kbps, the actual network bandwidth is usually less than 48 kbps due to traffic on the network [54]. Ideally, the system should continuously sense and update the current network bandwidth whenever bandwidth change occurs. However, the process of continuously sensing and updating the ever changing bandwidth is time and resource consuming as it involves sending data packets through the network. To solve this problem, we only sense the actual bandwidth at some points in time, and we use a fuzzy logic approach in conjunction with SWRL rules to predict the available bandwidth between these points. Also, to reason with bandwidth, we translate the predicted current bandwidth into meaningful symbolic values such as low, medium, and high bandwidth. Fuzzy logic is also used to predict the maximum size of learning resources that can be communicated without incurring long delays. For instance, we only search for learning resources with text type if a mobile device, operating on a GPRS network for instance, has very low bandwidth. However, we can extend media type to image and video if the network bandwidth is high and the available device memory is large. We also perform other checks to adapt to features such as screen resolution, used operating system, and network security. Below we describe the fuzzy logic approach used to predict media type and size and we give a full scenario to illustrate all system-centric adaptations.

We make use of the fuzzy logic truth values in conjunction with SWRL rules to allocate symbolic value to the predicted current network bandwidth. Figure 12 shows the membership function for network bandwidth. This is used to predict the network bandwidth using the fuzzy qualifying linguistic variables such as low, medium, and high. Note that Maxband which stands for maximum bandwidth is associated with the maximum connection speed of the used wireless adaptor. The symbol μ _A (x) represents a truth value that is between 0 and 1. Based on the membership function given in Figure 12, μ _A (x) can be computed by (1).

$\begin{array}{l}{\mathit{L}}_1:{\mathit{\mu }}_{\mathit{A}}\left(\mathit{b}\right)=1\\ {\mathit{L}}_2:{\mathit{\mu }}_{\mathit{A}}\left(\mathit{b}\right)=-\frac{\mathit{b}-\mathit{Mediumband}}{\mathit{Mediumband}-\mathit{Lowband}}\\ {\mathit{L}}_3:{\mathit{\mu }}_{\mathit{A}}\left(\mathit{b}\right)=\frac{\mathit{b}-\mathit{Lowband}}{\mathit{Mediumband}-\mathit{Lowband}}\\ {\mathit{L}}_4:{\mathit{\mu }}_{\mathit{A}}\left(\mathit{b}\right)=-\frac{\mathit{b}-\mathit{Highband}}{\mathit{Highband}-\mathit{Mediumband}}\\ {\mathit{L}}_5:{\mathit{\mu }}_{\mathit{A}}\left(\mathit{b}\right)=\frac{\mathit{b}-\mathit{Mediumband}}{\mathit{Highband}-\mathit{Mediumband}}\\ {\mathit{L}}_6:{\mathit{\mu }}_{\mathit{A}}\left(\mathit{b}\right)=1\end{array}$

(1)

In Rule-Set-1 we describe the SWRL rules that are used to infer the truth values of classified symbolic network bandwidth given in (1). For the sake of space we only give TruthValueRule1 and TruthValueRule2 which are respectively related to L ₁ and L ₂ of (1), and which are used to infer the truth values associated to low network bandwidth. The abstract SWRL syntax which is consistent with the OWL specification is rather verbose and not particularly easy to read [61]. Instead, we use a relatively informal “human readable” form where both antecedent and consequent are conjunctions of atoms with variables prefixed with a question mark (e.g., ?x), and which may also include functional notations as shown in Rule-Set-1. The latter shows the two above-mentioned rules which use the property UsedDevice(?act,?dev) which associates a learner identified by an activity identifier act to their handheld device dev, and the data properties HasBandwidth and MaxBandwidth which describe respectively the current network bandwidth and maximum connection speed of a used handheld device.

Rule-Set-2 Examples of truth value inferences

Ontology related facts

A1)   before applying TruthValueRule4

ActivityID(Irene) UsedDevice(Irene, MotoW270) HasBandwidth(MotoW270, 18.0) HasNetworkAdaptor(MotoW270, GPRS) MaxBand(MotoW270, 32.0) swrlb:multiply(?Highband,32.0,0.75) swrlb:multiply(?Mediumband, 32.0, 0.5) swrlb:greaterThan(18.0, 16.0) swrlb:lessThanOrEqual(18.0, 24.0) swrlb:subtract(?z1, 24.0, 18.0) swrlb:subtract(?z2, 24.0, 16.0) swrlb:divide(?z, 6.0, 8.0)

B1)   before applying TruthValueRule5

UsedDevice(Irene, MotoW270) HasBandwidth(MotoW270, 18.0) HasNetworkAdaptor(MotoW270, GPRS) MaxBand(MotoW270, 32.0) swrlb:multiply(?Highband,32.0,0.75) swrlb:multiply(?Mediumband, 32.0, 0.5) swrlb:greaterThan(18.0, 16.0) swrlb:lessThanOrEqual(18.0, 24.0) swrlb:subtract(?z1, 18.0, 16.0) swrlb:subtract(?z2, 24.0, 16.0) swrlb:divide(?z,2.0,8.0)

OInferred facts

A2)   after applying TruthValueRule4

ProbMedium(MotoW270, 0.75) NetworkBandwidth(MotoW270, "Medium")

B2)   after applying TruthValueRule5

ProbHigh(MotoW270, 0.25) NetworkBandwidth (MotoW270, "High")

It should be noted that the system is designed in such a way that the maximum tolerable response time, that depends on the user’s activity and the nature of requested resources, can be easily modified to accommodate learners with more or less restrictive time constraints. Figure 13 shows the main components of the used fuzzy system which consists of singleton fuzzifier, product inference engine, fuzzy rule base, and center average defuzzifier. The system starts with a fuzzification of the input variable, then rule evaluation, followed by aggregation. The latter is the process of unification of the outputs of all rules. The last step of the fuzzy system is defuzzification to obtain a crisp output [50]. Figure 14 shows the membership function for resource size.

The three fuzzy sets Low, Medium, and High describing predicted network bandwidth are used as an input space in the fuzzy system to predict the maximum allowable resource size. We also define three fuzzy sets Small, Medium, and Large as the output space (resource size) as shown in Figure 14. Note that we use SmallSize, MediumSize, and LargeSize to refer to center average values for small, medium, and large fuzzy sets respectively. The fuzzy rule base consists of three simple rules as shown below.

R ¹ : if network bandwidth (B) is Low then resource size (Z) is set to Small

R ² : if network bandwidth (B) is Medium then resource size (Z) is set to Medium

R ³ : if network bandwidth (B) is High then resource size (Z) is set to High

Suppose that the fuzzy set F ^k in the fuzzy rule base R ^k is normal with center ${\overline{\mathit{y}}}^{\mathit{k}}$. Then the crisp output from the fuzzy system with singleton fuzzifier, product inference engine, center average defuzzifier, and rule base R ^k where, R ^k is defined as follows: if x ₁ is A ₁ ^k and… and x _n is A _n ^k , then y is F ^k , k = 1,…N is given by [50]:

${\mathit{y}}^{*}=\frac{{\displaystyle \sum _{\mathit{k}=1}^{\mathit{N}}{\overline{\mathit{y}}}^{\mathit{k}}\left({\displaystyle \prod _{\mathit{i}=1}^{\mathit{n}}{\mathit{\mu }}_{{\mathit{A}}_{\mathit{i}}^{\mathit{k}}}\left({\mathit{x}}_{\mathit{i}}^{*}\right)}\right)}}{{\displaystyle \sum _{\mathit{k}=1}^{\mathit{N}}\left({\displaystyle \prod _{\mathit{i}=1}^{\mathit{n}}{\mathit{\mu }}_{{\mathit{A}}_{\mathit{i}}^{\mathit{k}}}\left({\mathit{x}}_{\mathit{i}}^{*}\right)}\right)}}$

(2)

In our case, (2) results into:

${\mathit{Z}}^{*}=\frac{{\displaystyle \sum _{\mathit{i}=1}^3\overline{{\mathit{Z}}_{\mathit{i}}}}*{\mathit{\mu }}_{{\mathit{A}}^{\mathit{i}}}\left({\mathit{B}}^{*}\right)}{{\displaystyle \sum _{\mathit{i}=1}^3{\mathit{\mu }}_{{\mathit{A}}^{\mathit{i}}}\left({\mathit{B}}^{*}\right)}}=\frac{\mathit{SmallSize}*{\mathit{\mu }}_{\mathit{Low}}\left({\mathit{B}}^{*}\right)+\mathit{MediumSize}*{\mathit{\mu }}_{\mathit{Medium}}\left({\mathit{B}}^{*}\right)+\mathit{L}arg\mathit{eSize}*{\mathit{\mu }}_{\mathit{L}arg\mathit{e}}\left({\mathit{B}}^{*}\right)}{{\mathit{\mu }}_{\mathit{Low}}\left({\mathit{B}}^{*}\right)+{\mathit{\mu }}_{\mathit{Medium}}\left({\mathit{B}}^{*}\right)+{\mathit{\mu }}_{\mathit{L}arg\mathit{e}}\left({\mathit{B}}^{*}\right)}$

(3)

In Rule-Set-3, we show the SWRL rule (ResourceSizeRule) associated with equation (3). In ResourceSizeRule, the data properties ProbLow, ProbMedium, and ProbHigh are those probabilities obtained in TruthValueRules (see Rule-Set-1). To show how these rules are applied in our system, we provide a real-life scenario. Let’s assume that our learner Irene is using a GPRS connection with a maximum connection speed of 32 kbps. This connection speed delimits the maximum resource size to 500Kbytes as described above. These assumptions are represented by facts (A1) in Rule-Set-3. When ResourceSizeRule is applied, fact (A2) is inferred resulting into the addition of statement ResourceSize(MotoW270, 281.25) to the list of facts. Indeed, since the previously sensed network bandwidth was 18 kbps, our system chooses not to exchange resources over 281.25Kbytes as deduced from the set of inferences shown in Rule-Set-3.

Rule-Set-4 describes the SWRL rules used to select the media type of retrieved learning resources based on predicted current bandwidth. The data properties NetworkBandwidth and AvailableMemory represent respectively the current bandwidth and available device memory. In MediaRule1-to-3, the system sets the media type to the appropriate format (i.e. text, image, video) based on the value of predicted current network bandwidth. The system also sets the maximum allowable resource size, as computed in Rule-Set-3, based on the device’s available memory. If the size of the device memory is smaller than the maximum allowable media size computed in Rule-Set-3, then AllowedResourceSizeRule1 sets the maximum media size to the device memory size; otherwise the maximum media size remains unchanged as stated in AllowedResourceSizeRule2.

Rule-Set-8 example of learning sequence construction

Ontology related facts

A1)   before applying SimilarResourceRule1:

ConductedLearningActivity(Irene,A1) MakeQuery(A1, Logical Express)

HasKeyword(Logical Express, C₂₈)

IsMappedTo(C₂₈,LR_28a)IsMappedTo(C₂₈, R_28b)

B1) before applying CoreResourceRule1

ConductedLearningActivity(Irene,A1) MakeQuery(A1, Logical Express)

HasKeyword(Logical Express, C₂₈)

HasNecessaryPart(C₂₈,C₂₉)

HasNecessaryPart(C₂₈,C₃₀)

HasNecessaryPart(C₂₈,C₃₁)

HasNecessaryPart(C₂₈,C₃₂)

¬Covered(Irene,C₃₁)

¬Covered(Irene,C₃₂)

IsMappedTo(C₃₁,LR_31a)

IsMappedTo(C₃₁,LR_31b)

IsMappedTo(C₃₂,LR_32a)

¬Consumed(Irene,LR_31b)

¬Consumed(Irene, LR_32a)

C1)   before applying NonCoreRelatedResourceRule2:

ConductedLearningActivity(Irene,A1)

MakeQuery(A1, Logical Express)

HasKeyword(Logical Express, C₂₈)

IsPartOf(C₂₈,C₅)

¬Covered(Irene,C₅)

IsMappedTo(C₅,LR_5a)

IsMappedTo(C₅,LR_5b)

IsMappedTo(C₅,LR_5a)

¬Consumed(Irene,LR_5a)

¬Consumed(Irene, LR_5c)

Inferred facts

A2)   after applying SimilarResourceRule1

SimilarLR(A1, LR_28a)

SimilarLR(A1, LR_28b)

B2)   after applying CoreResourceRule1

CoreLR(A1,LR_31b)

CoreLR(A1,LR_32a)

C2)   after applying NonCoreRelatedResourceRule2

NonCoreRelatedLR (A1,LR_5a)

NonCoreRelatedLR (A1,LR_5c)

To illustrate the reasoning mechanism adopted in the learner-adaptation process, we use a real life learning scenario based on the C++ Programming ontology shown in Figure 15. Let’s assume that learner Irene wants to learn about “logic expressions” of the C++ programming language. This query has similar keywords with the ontology concept C28 which describes “Logical_Expression” as shown in Figure 15. The reasoning engine is invoked by firing SimilarResourceRule1 which operates on facts (A1) and infers (A2) as shown in Rule-Set-8. The results of this inference consist of mapping learning resources LR28a and LR28b to concept C28. Facts (A2) are then added to the knowledge base. It should be noted here that concept C28 does not have any similar or prerequisite concepts in the C++ ontology. Therefore, the application of SimilarResourceRule2&3 and PrerequisiteResourceRule do not produce any useful results in this case. However, when applying CoreResourceRule on facts (B1), concepts C29(Boolean_data), C30(Relational_Operators), C31(Logical_Operators), and C32(Operators_Precedence) are inferred as core knowledge that need to be offered to the learner to fully understand the queried concept (i.e. Logical_Expressions). We also infer that concepts C29 and C30 have already been covered by Irene in previous studies, thus, the reasoner automatically eliminates them from the learning sequence. The system also infers that learning resources (LR31a, LR31b) and LR32a are mapped to concepts C31 and C32 respectively, and that learning resources LR31b and LR32a have not been consumed by Irene. These resources are therefore prescribed to Irene and facts (B2) are added to the knowledge base. Finally, NonCoreResourceRule2 is applied on facts (C1) to infer (C2) which states that concept C28(Logical_Expression) is part of C5(Selection), and that LR5a and LR5c which correspond to concept C5 have not been consumed by Irene so far, and therefore can be prescribed to her as non-core related knowledge. The learning sequence resulting from the application of the above mentioned rules produces the learning sequence shown in Figure 16 where the type of each learning resource (e.g. core, non-core, prerequisite, etc.) is clearly specified to the learner.

The learning path navigation algorithm, described below, is triggered once the learner starts interacting with the initially recommended learning sequence. The invocation of any learning resource leads to updating the list of consumed learning resources, and generating a new sub-learning path associated with the newly invoked concept. The new path is then added to the global learning path as shown in step 10 of the Learning_path_Navigation algorithm. The new sub-learning path is constructed by the learning_path_generation procedure, which first infers core concepts associated with the newly invoked concept and appends their respective resources. Then, for each core concept, it infers resources to fulfill their prerequisite knowledge. The learning path thus, grows dynamically as the learner invokes new concepts. However, when the learner makes a backward move, the corresponding sub-learning path is completely pruned from the global learning path. The learning session terminates when the learner either logs out of the system or consumes all prescribed learning resources.

In this paper, a personalized mobile learning system that provides lightweight services adapted to both system-centric and learner-centric context is proposed. In particular, an attempt was made to solve two challenging problems related to context-aware mobile learning. These are heterogeneous context perception and integration at the semantic level, and context-aware content discovery and adaptation at run-time. To deal with context heterogeneity, an upper ontology space is used to define a higher level unique semantic view of the learning scenario. We showed that knowledge embedded in the upper ontology can homogeneously be used to enable a unified reasoning mechanism that operates on facts instantiated by the perceived heterogeneous context elements. The paper also addressed the problem of context that is perceived with ambiguity by using fuzzy logic in conjunction with semantic web based reasoning. The proposed system is characterised by the fact that the reasoning engine translates context changes as they occur into new content adaptation constraints in the operating environment, thus enabling context-aware personalized learning services. A number of learning scenarios have been used to demonstrate the main functions of the proposed system. The experimental results have shown that the system successfully adapts the learning content based on the learner’s context and surrounding environment.

This research work can be extended in many ways. One possible extension is the use of Mashup technology to make it possible to use multiple search agents in order to retrieve learning resources from multiple repositories, thus enhancing learning-content provision. In addition, we are currently working towards the provision of secured lightweight learning services. Trusted web-services are crucial for mobile learning applications such as those related to telemedicine or corporate learning.