Nowadays technological advancement enables multiple education scenarios, like online learning and technology enhanced classroom learning. Both of these scenarios share a common level of knowledge about the learner and his or her learning preferences. This knowledge is limited and in most scenarios gathered via a learner survey. This situation limits system capability on delivering individualised learning experience as the learner sometimes is not able to define his or her learning style, actual preferences and other aspects. Learning session and learner context data enable more advanced adaptation in intelligent tutoring scenarios and deliver new analytical capabilities to the trainer in classroom learning. Learning context data can be captured via various means and from multiple data sources, like education institution systems and physical sensors. This paper proposes the learner context data model attributes identifies the data sources to fill this model and identifies possible techniques to enable this process automation.
Personalised learning has a potential to improve learning process and overall learning
results [
The Intelligent Tutoring Systems (ITS) [
Any learning session happens in defined, describable and observable environment,
which might be either physical or virtual, e.g., e-learning system. Such environment
could be an educational institution, corporate environment, home or any other place
that is suitable for education. Learning from process perspective [
To describe learning environment with various contexts Schmidt [
Context capturing. This activity collects information about the learner and the learning session conditions. For example, assigned tasks and personal information that is relevant to learning process, like learner’s previous experience, individual goals, cognitive style and other aspects. The context can be captured from multiple sources, therefore, it should be managed in a way that several applications can view, modify and update the context in a mutually enriching way.
Development and application of context aware methods. Current online learning platforms (both academic and enterprise) support continuous learning and have a strictly defined learning path. However, context aware systems enable on-demand learning by taking only context relevant learning objects. For example, when learner is performing some task and identifies that there is a need for additional information, the context aware system could assist and assess the situation and look for applicable and suitable learning material that is not necessarily a long lasting curriculum, but might be a short how-to guide on a particular subject. The context aware systems have great application potential in work/enterprise environment, where companies might have multiple knowledge bases and other information sources. Wiki pages, knowledge management systems are common systems for almost any company and they might contain large number of tagged information that is almost ready for context-aware applications.
Context-aware resource preparation. This activity is responsible for tutoring content preparation for further application in context-aware systems. The objective of this activity is to create context application-ready tutoring material including resource relationship definitions.
After context is captured, it is important to identify context quality level. If context
quality is low, it will not be applicable in classroom and/or intelligent tutoring systems.
Bellavista et al. [
The goal of this paper is to create context aware learner model and identify
corresponding context data sources for the analysed hybrid learning scenario, where
traditional classroom training is provided with additional learning materials available via
online ITS prototype [
The paper is organized as follows. Section II discusses the context modelling aspects. Section III presents the proposed context model. Section IV discusses the empirical results. 2
For any entity or process we can find unlimited amount of contexts and consequently
create unlimited number of context models. This situation requires to identify context
importance and relevance for a particular application and have a good understanding
about the context and the context elements. Perera et.al. [
Any context model can be static or dynamic, have its own lifecycle, level of trust,
relationships with other contexts, timelines and other attributes. Past research
approaches demonstrate generic context model development where context is classified
in three groups: user context, things context and system context [
Context modelling can be done by several techniques. Straong and Linnhoff-Popien
[
For situations where a single context is being used by multiple entities, context aware system should be able to identify to which entity this context applies, how to present it (order, structure, etc.), therefore, pre-defined application scenario and/or entity identification mechanisms should be implemented in the system.
Based on a context model application domain, they may describe general
information about the context and a context structure with corresponding relationships, or
be very specific and describe in details each context element internal structure,
behaviour and relationships with other context element. Which level of detail to choose
depends on the application domain and author’s/experts beliefs. Modelling can be done
in a various levels- conceptual, logical and physical. Physical modelling is not needed
when context components are integrated into existing system or this level is replaced
with system architecture. However, it is required to define how defined context results
will integrate into existing or newly created system or its components. In the learning
domain context modelling could be done in several directions; learning object context,
pedagogical context, student model context, learning session context, learning
environment context. Any of these contexts at some degree could be used to facilitate distance
learning and classroom process. This research focuses on ITS and classroom learning
hybrid scenario learner context model. Therefore, the proposed model is the first
iteration of continues model development and at this stage may not contain all possible
learning domain contexts and context attributes. The proposed model is based on a
student model proposed by Lukasenko [
All context categories and attributes are considered with the following parameters and their description is given in Table 1.
tively by the system author or based on calculated results Describe level of details.
Each context element has its own data source that needs to be identified. This is analytical activity where operation environment (virtual and/or physical) iteratively is reviewed as some data sources should be changed during system operation. After all available information about operation environment is gathered it needs to be evaluated by following several steps: 1st step identifies all physical and virtual sensors. As a virtual sensor might serve existing service, database, it system or application. 2nd step in case if context is composed from multiple sensors, a single logical sensor is created, where author defines sensor data merging/transformation mechanism and result data structure of this sensor. 3rd step defines data source reliability and confidence rank, desired data precision and granularity. 3
The proposed context model is an object that consists of several sub objects which corresponds to the contexts/ context dimensions and is designed to fill in and/or verify learner model attributes. This model for classroom learning will provide more detailed information to trainer for further and more detailed student result analysis and for ITS model it will serve as additional source of information for personalised learning content delivery. The model will describe various aspects of student model. As a first step the set of learner model attributes that could be filled in from context models is proposed (see Table 2). Each learner model category represents learner context sub contexts which are part of the proposed context model. model
A learner’s learning style (theory oriented, task oriented, problem solving oriented) Learner category Contact information
Current knowledge skills Use of system
and in a problem domain Topics reviewed/ tasks completed in a course Attempts made/ time spent model
problem domain before beginning of a course The course topics that the learner has viewed/studied. The tasks that the learner has solved.
The number of attempts committed by the learner to solve a task, and the time spent on studying a topic 3.1
Identified Data Sources The proposed context aware learner model attributes could be filled in either manually by system author or by semi-automatic/automatic way based on data of another system or data store. Analysed learning scenario is taking place at Riga Technical University where classroom training is supported by ITS prototype. University as educational institution has multiple IT systems with various functionality. For this scenario particular interest is in an annual survey system, study system that stores information about the learner’s performance, ITS prototype and e-learning platform Moodle (see Table 3).
The attributes of the proposed learner context model are analysed according to the parameters listed in Table 1, namely, attribute precision, reliability, granularity/resolution, data source confidence rank, data age and sensor type: • • • • •
Learner identification information (name, surname, IDs, email address). This combination of attributes is precise, reliable, verifiable and detailed. The data for these attributes come from university study system which is a fully trustful data store. The only exception is the email address, that is subject of occasional change. Information is captured via virtual sensors.
Learning style. The learning style is of low precision, subject of rapid change, hard to verify and not detailed. Learning style is based on various primary and secondary contexts and data interpretation algorithms. Data sources for learning style are multiple mostly system logs. In this research these data sources are ITS prototype and Moodle log as well as annual learners’ survey. The information is captured from various virtual and logical sensors.
Knowledge level in a problem domain. The knowledge level is a subject of change and has medium level of precision; information is reliable depending on the type of knowledge assessment level of information resolution may vary. Data source confidence rank is from medium to fully trustful as the sources of information are university study system (fully trustful) and ITS knowledge assessment modules where level of trust is medium. Similar to learning style attribute, the data may come from virtual and logical sensors.
Topics reviewed/ tasks completed in a course. This attribute is based on systems logs (ITS and Moodle), is trustful, precise and has high level of details. Data source is reliable and data is up-to-date. Information comes from virtual sensors. Attempts made/ time spent. This attribute is similar to topics reviewed/tasks completed and shares the same attribute characteristics. 4
Research discussed in this paper follows the 3 main context aware learning activities (see Section 1) by defining context capturing aspects, identifies context data source level of trust for further application scenarios in the classroom and intelligent tutoring system supported learning. The analysis of empirical available data set (see Table 3) showed, that it is possible to use this contextual data for several proposed learner model attributes semiautomatic/automatic filling. However, to automate this process there is a need for an initial effort on a data source annotation, metadata preparation, context models and context relationship definition. More advanced automation scenarios could be enabled by implementing reasoning techniques. For a small number of learners’ and where high level context model description is satisfactory, learner context model can be filled with data manually by the trainer and/or expert. For the analysed context application scenario corresponding tooling is required. Additionally, the analysed data contain verifiable information, for example, the number of attended classroom lectures, learner’s employment status that might affect overall learner performance and provide additional information about the learner’s level of knowledge ( for example, experience with programming languages and other relevant information), therefore, some physical sensor and/or software for learner direct and indirect tracking solution/system and integration with other external systems (for example, employment and industry related skill set might come from LinkedIn social network) is required.