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Comparative Study of Educational Process Construction Supported by an Intelligent Tutoring System

2.1. Introduction

One of the evolving areas that will certainly occupy computer scientists over the next decade is the computer-supported learning environment. The latter is increasingly mediated by new technologies of information and communication. In fact, it can provide various kinds of technological support to guide teaching and learning (Rathore and Arjaria 2020). This context is therefore considered to be multidisciplinary as it includes computer science, cognitive psychology, pedagogy, didactics and educational sciences.

Throughout the years, there have been different types of environments, including the traditional e-learning system, instructional design system and intelligent tutoring system (Tzanova 2020). The latter is increasingly gaining popularity in the academic community due to its multiple learning benefits. In fact, most of these systems allow only the application of content adaptation and ignore the adaptation of educational processes (learning process and pedagogical process) (Bayounes et al. 2019). This is a major constraint for providing personalized learning paths and appropriate learning content and for exploiting the richness of individual differences in learning needs and pedagogical preferences (Bayounes et al. 2014). Therefore, this work more particularly concerns process adaptation in intelligent tutoring systems. In reality, personal learning paths are the best solution because students can more effectively acquire and retain knowledge and skills that will help them in the real world (Olesea 2019).

In fact, the main research problem is the construction of the learning process in ITS. The study of this problem leads to the extension of the construction in order to take into consideration the teaching process, also called the pedagogical process, which is strongly correlated with it. In the rest of this chapter, the term “educational processes” will be used to indicate both learning and pedagogical processes.

Within this context, a literature survey has been undertaken using a framework of educational process construction. The comparison framework is used in many engineering works in the literature and has proven its effectiveness in improving the understanding of various engineering disciplines (method engineering, process engineering) (Rolland 1998). By adopting the multi-level view of educational process definition, the goal of the proposed framework is to identify the suitable construction approach supported by an ITS in order to satisfy the individual learning needs and to respect the system constraints.

This research aims at providing an appropriate educational process that fits the needs of the learners, the preferences of the tutors and the requirements of the administrators of the intelligent tutoring system (ITS). The chapter thus starts by exploring the theory in order to study the problem of educational process construction. This study proposes a new multi-level view of educational processes. Based on this view, a faceted definition framework conducts a comparative study between different ITS to understand and classify issues in educational process construction. Finally, section 2.5 concludes this work with our contribution and research perspectives.

2.2. New view of educational process

The most important task of this research is to study the different definitions of educational processes. In fact, a valid process definition ensures valid process modeling to support an appropriate process adaptation (Chang et al. 2020).

The analysis of this study highlights the specification of multiple new levels of educational process definition (see Figure 2.1). These levels are divided into the psycho-pedagogical level, the didactic level, the situational level and the online level (Bayounes et al. 2020). The first two levels define the educational process in the theoretical layer. The last two levels define the practical layer. Both layers consider the correlation between pedagogical and learning facets.

This multi-level view specifies the guidance/alignment relationships between the learning and pedagogical facets and the instantiation/support relationships between various levels.

The different definition levels are specified by three major components (affective, cognitive and metacognitive). The first component defines the affective objective achieved by the educational process (why?). The second component defines the cognitive product adopted by the process (what?). The third component includes the metacognitive process that is used to achieve the affective objective (how?).

Schematic illustration of multi-level view of educational processes.

Figure 2.1 Multi-level view of educational processes (Bayounes et al. 2020).

2.2.1. Psycho-pedagogical level

At this level, the pedagogical and the learning view of the process are not defined. The educational process is viewed as a psycho-pedagogical change that occurs by using the educational process meta model. The latter adopts various subjects, which are defined by different theories of learning paradigm.

2.2.2. Didactic level

At this level, the process is specified by adopting the interaction between the pedagogical and the learning view. By considering the latter, the process model is used to achieve learning outcomes by considering the constraints of the learning domain. For the pedagogical view, the process is viewed as a pedagogical goal achieved via defining a process model based on pedagogical content.

2.2.3. Situational level

This is the level of the instantiation of process models by considering the different characteristics of the learning/teaching situation in order to reach the desired learning level through the use of different learning objects. In fact, the situation characteristics are the objective, the different tasks and the different available resources.

2.2.4. Online level

This is the level of the execution of different learning and pedagogical actions that are supported by different learning systems. This execution is achieved by adopting different electronic media in order to use the different learning objects.

2.3. Definition framework

The issue of adaptation in ITS has become a crucial topic of research in recent years. With the emergence of ITS, it has become possible to provide a learning process that matches the learner characteristics, the pedagogical preferences and the specific learning needs. In this regard, a literature survey has been conducted by using a framework of educational process construction (see Figure 2.2).

Schematic illustration of framework worlds.

Figure 2.2 Framework worlds (Bayounes et al. 2012)

2.3.1. Didactic domain world

This is the world of processes taking into account the notion of the process and its nature (Bayounes et al. 2012). The didactic domain world contains the knowledge of the domain about which the proposed process has to provide learning (Bayounes et al. 2012). This world specifies the process nature by reviewing the domain dimensions. The nature view defines three facets, namely pedagogy, learning and process.

The pedagogy facet presents three attributes: the pedagogical orientation, the pedagogical method and the correlation. The different orientations are teacher-directed, learner-directed and teacher–learner negotiated. The pedagogical method specifies the activities of the learning process. It can be classified as direct instruction, indirect instruction, interactive instruction, experiential learning and independent study. The third attribute tests the correlation between the pedagogical and learning processes.

The learning facet presents three attributes: the performance, the learning mode and the learning outcome. The different performance types are remember, use and find (Merrill 1983). These performances are accomplished by three different learning modes, namely accretion, structuring and tuning (Rumelhart and Noorman 1978).

2.3.2. Instructional design world

This world deals with the representation of processes by adopting predefined models. It focuses on the description view by respecting the notation conditions and the constraints of representation level. This world includes the notation and the level of process description.

In the notation facet, four attributes are found: the type, the form, the content and the models. The two notation types are standard or proprietary. These types are used by adopting three major forms namely informal, semi-formal and formal. According to the component display theory (Merrill 1983), these forms present four major types of content. These types are fact, concept, procedure and principle. These contents are used to instantiate the domain model, the learner model and the pedagogical model.

Furthermore, the level facet presents three attributes: the granularity, the modularization and the coverage. The different levels of granularity are course, sequence of activities and simple activity. Three types of modularization, namely primitive, generic and aggregation, define these levels (Kumar and Ahuja 2020). In fact, the modularization attribute is used to capture the laws governing the learning process construction by using process modules (Bayounes et al. 2012). This process has been defined differently in different coverage (Bayounes et al. 2014):

The activity: the process is defined as a related set of activities conducted for the specific purpose of product definition.

The product: the process is a series of activities that cause successive product transformations to reach the desired product.

The decision: the process is defined as a set of related decisions conducted for the specific purpose of product definition.

The context: the process is a sequence of contexts causing successive product transformations under the influence of a decision taken in a context.

2.3.3. Learning environment world

This world deals with the entities and activities that arise as part of the engineering process itself (Bayounes et al. 2012). It focuses on the design view by describing the implementation of process representation. The design view includes five facets, namely context, construction, optimization, guidance and adaptation.

The first facet defines the type and the form of context. The construction facet deals with four issues: the approaches, the methods, the tools and the techniques of adaptive learning process construction. In a manner analogous to the adaptation spectrum (Patel and Kinshuk 1997), we can organize construction approaches in a spectrum ranging from “low” to “high” flexibility. The construction approach attribute is defined as the approach: ENUM {Rigid, Contingency, On-The-Fly}. These approaches are adopted by using the instantiation, the assembly and the ad hoc method. These methods apply three major techniques (curriculum sequencing, intelligent solution analysis, problem-solving support).

In the optimization facet, four attributes are found: the model, the method, the technique and the parameters of optimization. The different models are map-based, network-based and tree-based. Three types of methods, namely exact, heuristic and meta-heuristic, apply these models. These methods use rule-based and case-based techniques. The achieved educational intentions can be used to implement these optimization techniques.

In addition, the guidance facet defines four attributes: the type, the method, the outcomes and the parameters of guidance. The guidance types are strict and flexible. These types make use of three major methods, namely expository, collaborative and discovery. The outcomes of these methods are activity, resource and intention. To this end, the guidance methods adopt three parameters, namely learning style, cognitive state and teaching style.

Finally, the fourth facet introduces the adaptation by defining five attributes, which are the dimension, the position, the method, the technique and the parameters of adaptation. The three dimensions of adaptation are content, structure and presentation. The position is identified by adopting the adaptation spectrum (Patel and Kinshuk 1997). Each position is satisfied by applying four major methods, namely macro adaptive, aptitude treatment interaction, micro adaptive and constructivist-collaborative (Garcia-barrios et al. 2004). These methods are implemented by four major techniques, which are adaptive interaction, adaptive course delivery, content discovery and assembly and adaptive collaboration support (Paramythis and Loidl-Reisinger 2004). These techniques adopt four major parameters, specifically learning goal, learning history, prior knowledge and system information.

2.3.4. Learning situation world

This world supports the scenario view by examining the reason and the rationale of learning process engineering (Bayounes et al. 2012). It describes the organizational environment of the educational process by indicating the purpose and policy process management. The purpose facet includes two attributes: process and learning. In fact, the construction approaches have been designed for different purposes and try to describe the learning process in different attitudes: descriptive, prescriptive and explanatory. The learning purpose defines the level acquired by constructing the learning process. According to Bloom’s taxonomy (Forehand 2012), the different levels are knowledge, comprehension, application, analysis, synthesis and evaluation.

The second attribute identifies three policies of process management, particularly evolving, reuse and assessment. This attribute supports the validation of construction process quality. Moreover, since the learning situations change and evolve over time, it is essential that the learning process construction supports these evolutions (Bayounes et al. 2012). As with any process development, the reuse and the assessment, which can occur at any stage of learning process construction and at any level of abstraction and may involve any element of design and/or implementation, are outstanding.

2.4. Comparative study

2.4.1. Study scope

Several intelligent tutoring systems have been reported in the literature. Before applying our framework of educational process construction to these systems, Table 2.1 presents a brief description of selected systems. Indeed, this selection is based on:

  • various didactic domains;
  • different techniques of artificial intelligence;
  • several countries from different continents;
  • number and quality of related publications.

Table 2.1. Role solver participant

Intelligent tutoring systems
IDNameDidactic domainCountry
ITS 1ITS-CLinguisticsSpain
ITS 2PEGASEVirtual realityFrance
ITS 3CIRCISM-TutorMedicineUSA
ITS 4BitsProgrammingCanada
ITS 5SQL-TutorDatabaseNew Zealand

2.4.2. Description of systems

2.4.2.1. ITS-C (ITS 1)

The Intelligent Tutoring System based on Competences (ITS-C) extends an ITS by linking the latter and the pedagogical model based on Competency-based Education (Badaracco and Martínez 2013). It adopts Computerized Adaptive Tests (CAT) as common tools for the diagnosis process (Badaracco and Martínez 2013).

2.4.2.2. PEGASE (ITS 2)

The PEdagogical Generic and Adaptive SystEm (PEGASE) is used to instruct the learner and to assist the instructor. This system emits a set of knowledge (actions carried out by the learner, knowledge about the field, etc.), which PEGASE uses to make informed decisions (Buche et al. 2010).

2.4.2.3. CIRCSIM-Tutor (ITS 3)

CIRCSIM-Tutor is an intelligent tutoring system for teaching the baroreceptor reflex mechanism of blood pressure control to first-year medical students (Glass 2001).

2.4.2.4. BITS (ITS 4)

The Bayesian Intelligent Tutoring System (BITS) is a web-based intelligent tutoring system for computer programming using Bayesian technology (Butz et al. 2004).

2.4.2.5. SQL-Tutor (ITS 5)

The SQL-Tutor is a problem-solving environment intended to complement classroom instruction, and we assume that students are already familiar with database theory and the fundamentals of SQL (Mitrovic et al. 2000). This ITS adopts constraint-based modeling as a learner modeling approach.

2.4.3. Specification of approaches

2.4.3..1 Approach of ITS 1

2.4.3.1.1. Nature view

The system adopts the learner-directed orientation by using the indirect and the independent methods (see Table 2.2). The system is used to achieve the intellectual skill and the cognitive strategy by applying the using and the finding performance. In fact, this system supports a tactic and linear process by applying the structuring and tuning modes.

Table 2.2. Nature view of ITS 1

FacetDefinition
AttributeValues
PedagogyOrientation{Learner-Directed}
Method{Indirect, Independent Study}
Correlation{Not Considered}
LearningPerformance{Use, Find}
Mode{Structuring, Tuning}
Outcome{Intellectual Skill, Cognitive Strategy}
ProcessType{Tactic}
Form{Linear}
2.4.3.1.2. Description view

By adopting the learner model, the pedagogical model and the domain model, the system adopts a standard and formal notation of the learning process (see Table 2.3). This notation is used to describe the required concepts. By using the aggregation, the system uses a description of the activities sequence to cover the desired product.

Table 2.3. Description view of ITS 1

FacetDefinition
AttributeValues
NotationType{Standard}
Form{Formal}
Content{Concept}
Models{Domain Model, Learner Model, Pedagogical Model}
LevelPerformance{Sequence}
Mode{Aggregation}
Outcome{Product}
2.4.3.1.3. Design view

By respecting an uncertain and an evolved context, the system supports the contingency approach. This construction approach applies the assembly methods by adopting the problem-solving support technique (see Table 2.4).

The system endorses a network-based model of optimization by defining an exact method. This method is specified by using different rules. Moreover, the system supports a flexible guidance by employing discovery methods to provide the suitable activity. For that, these methods consider the cognitive state. To offer a structure-based adaptation, the system uses the method aptitude–treatment interaction by adopting the technique content discovery and assembly. This technique considers the prior knowledge to satisfy the position system-initiated adaptivity with pre-information to the user about the change.

Table 2.4. Design view of ITS 1

FacetDefinition
AttributeValues
ContextType{Uncertain}
Form{Evolved}
ConstructionApproach{Contingency}
Method{Assembly}
Technique{Problem-Solving Support}
OptimizationModel{Network-Based}
Method{Exact}
Technique{Rule-Based}
Parameters{Not Defined}
GuidanceType{Flexible}
Method{Discovery}
Parameters{Cognitive State}
Outcome{Activity}
AdaptationDimension{Structure}
Position{System-Initiated Adaptivity with pre-information}
Method{Aptitude–Treatment Interaction}
Technique{Content Discovery and Assembly}
Parameters{Prior Knowledge}
2.4.3.1.4. Scenario view

To achieve the evaluation level, the system supports a prescriptive process of learning. The reuse and the assessment of the process are considered (see Table 2.5).

Table 2.5. Scenario view of ITS 1

FacetDefinition
AttributeValues
PurposeProcess{Prescriptive}
Learning{Evaluation}
PolicyReuse{True}
Evolving{False}
Assessment{True}

2.4.3.2. Approach of ITS 2

2.4.3.2.1. Nature view

The system adopts the learner-directed and the teacher–learner negotiated pedagogical orientations by implementing the experiential pedagogical methods (see Table 2.6). By considering the pedagogical correlation, the system is used to reach the desired cognitive strategy. This system supports a strategic and linear process of learning through applying the structuring and the tuning modes. These modes are adopted to achieve the using and the finding performance.

Table 2.6. Nature view of ITS 2

FacetDefinition
AttributeValues
PedagogyOrientation{Learner-Directed, Teacher–Learner Negotiated}
Method{Experiential}
Correlation{Considered}
LearningPerformance{Use, Find}
Mode{Structuring, Tuning}
Outcome{Cognitive Strategy}
ProcessType{Strategic}
Form{Linear}
2.4.3.2.2. Description view

The system applies a standard and formal notation of the learning process by using the learner model, the domain model and the pedagogical model (see Table 2.7). This notation is used to describe the appropriate procedure. By using the aggregation, the system employs a description of the activities sequence to meditate over the context.

Table 2.7. Description view of ITS 2

FacetDefinition
AttributeValues
NotationType{Standard}
Form{Formal}
Content{Procedure}
Models{Domain Model, Learner Model, Pedagogical Model}
LevelPerformance{Sequence}
Mode{Aggregation}
Outcome{Context}
2.4.3.2.3. Design view

By considering a certain and an evolved context of construction, the system supports the on-the-fly approach. This construction approach applies ad hoc methods by adopting the intelligent solution analysis technique (see Table 2.8).

By adopting the achieved learning intention, the system applies an exact method of optimization, by defining different rules. In addition, the system supports a flexible guidance by using discovery and expository methods to provide the suitable activity. For that, these methods consider the cognitive state. To support a presentation-based adaptation, the system uses the constructivist-collaborative method by applying the technique adaptive collaboration support. This technique considers the learning history to offer the position system-initiated adaptivity with pre-information to the user about the change.

Table 2.8. Description view of ITS 2

FacetDefinition
AttributeValues
ContextType{Certain}
Form{Evolved}
ConstructionApproach{On-The-Fly}
Method{Ad hoc}
Technique{Intelligent Solution Analysis}
OptimizationModel{Not Defined}
Method{Exact}
Technique{Rule-Based}
Parameters{Achieved Learning Intention}
GuidanceType{Flexible}
Method{Discovery, Expository}
Parameters{Cognitive State}
Outcome{Activity}
AdaptationDimension{Presentation}
Position{System-Initiated Adaptivity with pre-information to the user about the change}
Method{Constructivist-Collaborative}
Technique{Adaptive Collaboration Support}
Parameters{Learning History}
2.4.3.2.4. Scenario view

To achieve the evaluation level, the system supports a prescriptive process of learning. The reuse and assessment of the process are considered (see Table 2.9).

Table 2.9. Scenario view of ITS 2

FacetDefinition
AttributeValues
PurposeProcess{Descriptive, Explanatory}
Learning{Comprehension, Application}
PolicyReuse{False}
Evolving{True}
Assessment{True}

2.4.3.3. Approach of ITS 3

2.4.3.3.1. Nature view

The system adopts the teacher–learner negotiated orientation by using the indirect and interactive pedagogical methods (see Table 2.10). The system is used to reach the desired cognitive strategy and the suitable intellectual skill. For that, the system supports a tactic and linear process of learning by applying the structuring mode. This learning mode is applied to achieve the different types of performance.

Table 2.10. Nature view of ITS 3

FacetDefinition
AttributeValues
PedagogyOrientation{Teacher–Learner Negotiated}
Method{Indirect, Interactive}
Correlation{Not considered}
LearningPerformance{Remember, Use, Find}
Mode{Structuring}
Outcome{Intellectual Skill, Cognitive Strategy}
ProcessType{Tactic}
Form{Linear}
2.4.3.3.2. Description view

The system applies a proprietary and informal notation of the learning process by using the learner model and the domain model (see Table 2.11). This notation is adopted to describe the required procedure. By using the aggregation, the system supports an activity description of the learning process.

Table 2.11. Description view of ITS 3

FacetDefinition
AttributeValues
NotationType{Proprietary}
Form{Informal}
Content{Procedure}
Models{Domain Model, Learner Model}
LevelPerformance{Activity}
Mode{Aggregation}
Outcome{Activity}
2.4.3.3.3. Design view

The system adopts the on-the-fly approach by applying ad hoc methods. This approach supports a certain and stable context of construction through the application of the problem-solving support technique (see Table 2.12).

The system supports an exact method by designing different rules of optimization. Moreover, it adopts a flexible guidance by using collaborative methods to provide the suitable activity. For this purpose, these methods consider the cognitive state. In addition, the system provides a content-based adaptation by using the constructivist-collaborative method. By using the technique adaptive collaboration support, the method adopts the prior knowledge to satisfy the position system-initiated adaptivity with pre-information to the user about the change.

Table 2.12. Description view of ITS 3

FacetDefinition
AttributeValues
ContextType{Certain}
Form{Stable}
ConstructionApproach{On-The-Fly}
Method{Ad hoc}
Technique{Problem-Solving Support}
OptimizationModel{Not Defined}
Method{Exact}
Technique{Rule-Based}
Parameters{Not Defined}
GuidanceType{Flexible}
Method{Collaborative}
Parameters{Cognitive State}
Outcome{Activity}
AdaptationDimension{Content}
Position{System-Initiated Adaptivity with pre-information}
Method{Constructivist-Collaborative}
Technique{Adaptive Collaboration Support}
Parameters{Prior Knowledge}
2.4.3.3.4. Scenario view

The system supports a prescriptive process of learning to achieve the comprehension level. For that, the assessment and the reuse of the process are considered (see Table 2.13).

Table 2.13. Scenario view of ITS 3

FacetDefinition
AttributeValues
PurposeProcess{Prescriptive}
Learning{Comprehension}
PolicyReuse{True}
Evolving{False}
Assessment{True}

2.4.3.4. Approach of ITS 4

2.4.3.4.1. Nature view

By considering the learner-directed orientation, the system adopts the direct pedagogical methods (see Table 2.14). The system is used to reach the desired cognitive strategy, the required intellectual skill and the appropriate verbal information. For this purpose, the system supports a tactic and linear process of learning. This process applies the different learning modes to achieve the using and the remembering performance.

Table 2.14. Nature view of ITS 4

FacetDefinition
AttributeValues
PedagogyOrientation{Learner-Directed}
Method{Direct}
Correlation{Not considered}
LearningPerformance{Remember, Use}
Mode{Accretion, Structuring}
Outcome{Verbal Information, Intellectual Skill, Cognitive Strategy}
ProcessType{Tactic}
Form{Linear}
2.4.3.4.2. Description view

The system applies a standard and formal notation of the learning process by using the learner model and the domain model (see Table 2.15). This notation is used to describe the required concepts. The system uses an aggregation or primitive description of learning activity.

Table 2.15. Description view of ITS 4

FacetDefinition
AttributeValues
NotationType{Standard}
Form{Formal}
Content{Concept}
Models{Domain Model, Learner Model}
LevelPerformance{Activity}
Mode{Aggregation, Primitive}
Outcome{Activity}
2.4.3.4.3. Design view

The system supports the contingency approach to consider uncertain and stable contexts. This approach applies the instantiation methods of construction by adopting the curriculum sequencing technique (see Table 2.16).

By considering the network-based model, the system adopts an exact method of optimization by defining different rules. Moreover, the system supports a flexible guidance by using expository methods to offer the suitable resource. To this end, these methods consider the cognitive state. To support a structure and content-based adaptation, the system uses the method aptitude–treatment interaction by applying the adaptive course delivery technique. This technique adopts the learning goal and the prior knowledge to offer the position user selection adaptation for system suggested features.

Table 2.16. Description view of ITS 4

FacetDefinition
AttributeValues
ContextType{Uncertain}
Form{Stable}
ConstructionApproach{Contingency}
Method{Instantiation}
Technique{Curriculum Sequencing}
OptimizationModel{Network-Based}
Method{Exact}
Technique{Rule-Based}
Parameters{Not Defined}
GuidanceType{Flexible}
Method{Expository}
Parameters{Cognitive State}
Outcome{Resource}
AdaptationDimension{Content, Structure}
Position{User Selection Adaptation for system suggested features}
Method{Aptitude–Treatment Interaction}
Technique{Adaptive Course Delivery}
Parameters{Learning Goal, Prior Knowledge}
2.4.3.4.4. Scenario view

The system supports a descriptive process of learning to achieve the knowledge and the comprehension level. For this purpose, the reuse and the evolving of the process are adopted by the system (see Table 2.17).

Table 2.17. Scenario view of ITS 4

FacetDefinition
AttributeValues
PurposeProcess{Descriptive}
Learning{Knowledge, comprehension}
PolicyReuse{True}
Evolving{True}
Assessment{False}

2.4.3.5. Approach of ITS 5

2.4.3.5.1. Nature view

The system adopts the learner-directed orientation to support the independent study and the indirect pedagogical methods (see Table 2.18). The system is employed to reach the desired cognitive strategy by supporting a tactic and linear process of learning. The latter applies the structuring and tuning modes to achieve the using and the finding performance.

Table 2.18. Nature view of ITS 5

FacetDefinition
AttributeValues
PedagogyOrientation{Learner-Directed}
Method{Indirect, Independent Study}
Correlation{Not considered}
LearningPerformance{Use, Find}
Mode{Structuring, Tuning}
Outcome{Cognitive Strategy}
ProcessType{Tactic}
Form{Linear}
2.4.3.5.2. Description view

The system adopts a proprietary and informal notation of the learning process via the implementation of the learner model, the pedagogical model and the domain model (see Table 2.19). This notation is adopted to describe the appropriate procedure.

Table 2.19. Description view of ITS 5

FacetDefinition
AttributeValues
NotationType{Proprietary}
Form{Informal}
Content{Procedure}
Models{Domain Model, Learner Model, Pedagogical Model}
LevelPerformance{Activity}
Mode{Primitive}
Outcome{Product}
2.4.3.5.3. Design view

The system adopts the on-the-fly approach to consider the certain and stable context of construction. In fact, it supports ad hoc methods by applying the intelligent solution analysis technique (see Table 2.20).

The system adopts an exact method by specifying different rules. In addition, the system adopts a flexible guidance by using discovery methods to provide the appropriate resource. To this end, these methods incorporate a cognitive state. Furthermore, the system offers a content-based adaptation by implementing the aptitude–treatment interaction method. This method adopts the adaptive interaction technique in light of the learning history. In fact, this method is used to support the position of system-initiated adaptivity with pre-information to the user about the change.

Table 2.20. Description view of ITS 5

FacetDefinition
AttributeValues
ContextType{Certain}
Form{Stable}
ConstructionApproach{On-The-Fly}
Method{Ad hoc}
Technique{Intelligent Solution Analysis}
OptimizationModel{Network-Based}
Method{Exact}
Technique{Rule-Based}
Parameters{Not Defined}
GuidanceType{Flexible}
Method{Discovery}
Parameters{Cognitive State}
Outcome{Resource}
AdaptationDimension{Content}
Position{System-Initiated Adaptativity with pre-information}
Method{Aptitude–Treatment Interaction}
Technique{Adaptive Interaction}
Parameters{Learning History}
2.4.3.5.4. Scenario view

The system endorses an explanatory process of learning to achieve the comprehension and the application level. For this purpose, the assessment and the evolving of the process are supported by the system (see Table 2.21).

Table 2.21. Scenario view of ITS 5

FacetDefinition
AttributeValues
PurposeProcess{Explanatory}
Learning{Comprehension, Application}
PolicyReuse{False}
Evolving{True}
Assessment{True}

2.4.4. Study results and discussion

The framework analysis identifies the following main drawbacks of existing adaptive construction approaches. It should be noted that construction approaches are not sufficiently automated to automatically derive the achievement of objectives from the strategic process.

For the nature view, the different systems do not adopt all pedagogical methods to achieve the different types of process outcomes. In fact, the outcomes attitude and motor skill are not taken into consideration. In addition, the different systems support the tactic and linear processes of learning.

For the description view, most of the systems try to support the learner model, the domain model and the pedagogical model by applying proprietary notation, which can manifest some standardization problems. This notation is not used to specify a process description by respecting the activity level. Indeed, the context coverage is not supported by the learning process description.

For the design view, the uncertain and the evolved context of construction are not supported by the systems. The different on-the-fly approaches do not support a combined construction technique of learning processes to implement the instantiation methods. Most of these approaches are not implemented by applying a MAP-based model of optimization. For that, the educational intentions are not defined as optimization parameters. The construction approaches themselves are not sufficiently guided by considering the different methods and parameters. In fact, the flexible guidance is most used by the expository methods. However, this flexible guidance does not adopt the intention as outcome by considering the learning style and the teaching style.

Finally, the different systems do not apply micro methods by respecting system information to support process adaptation.

Therefore, the comparative study identifies the following limits:

– the adaptive ability of most of these ITS is limited to the content. In fact, the process-oriented adaptation is not supported by the different systems;

– the consideration of the educational process as a strategic and not linear one is limited;

– the flexible guidance of educational processes does not adopt the intention as outcome by considering the learning style and the teaching style;

– the different systems did not respect the evolving process policy.

2.5. Conclusion and future works

This work presents a new multi-level view of educational process definition. This view is introduced to depict a comparison framework, which has allowed identification of the characteristics and drawbacks of some existing construction approaches supported by ITS. This framework considers adaptive educational process engineering from four different but complementary points of view (Nature, Description, Design and Scenario). Each view allows us to capture a different aspect of learning process engineering (Bayounes et al. 2019).

As a matter of fact, the framework is applied to respond to the following purposes: to have an overview of the existing construction approaches, to identify their drawbacks and to analyze the possibility of proposing a better approach.

In order to study, understand and classify different construction approaches of educational processes in their diversity, we selected different ITS from five continents. After the use of the proposed framework, the first view considers that the different systems do not adopt all pedagogical methods. The second view identifies that the context coverage is not depicted. In fact, the uncertain context of construction is not supported by most of the systems. In addition, the evolving process policy is not respected.

The analysis of existing approaches definition identifies the lack of an adaptive and guided construction of educational processes that embody the new technologies of artificial intelligence. It should be noted that the majority of ITS do not respect the correlation between pedagogical and learning processes. Our future work will include the learner’s motivation (Dunn and Kennedy 2019) as a success factor of educational process construction.

2.6. References

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  3. Bayounes, W., Saâdi, I.B., Kinshuk, K., Ben Ghézala, H. (2013). An intentional model for learning process guidance in adaptive learning system. Proc. 22nd IBIMA Conference, Rome, Italy, 1476–1490.
  4. Bayounes, W., Saâdi, I.B., Kinshuk, K., Ben Ghézala, H. (2014). An intentional model for pedagogical process guidance in adaptive learning system. Proc. 23rd IBIMA Conference, Valencia, Spain, 1211–1227.
  5. Bayounes, W., Saâdi, I.B., Ben Ghézala, H. (2019). Educational processes’ guidance based on evolving context prediction in intelligent tutoring systems. Universal Access in the Information Society, 8(68), 1–24.
  6. Bayounes, W., Saâdi, I.B., Ben Ghézala, H. (2020). Definition framework of educational process construction supported by an intelligent tutoring system. Proceedings Multi-Conference OCTA (Organization of Knowledge and Advanced Technologies), Tunis, Tunisia [Online]. Available at: https://multiconference-octa.loria.fr/multiconference-program/.
  7. Buche, C., Bossard, C., Querrec, R., Chevaillier, P. (2010). {PEGASE}: A generic and adaptable intelligent system for virtual reality learning environments. IJVR, 9(2), 73–85.
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Note

  1. Chapter written by Walid BAYOUNES, Inès BAYOUDH SÂADI and Hénda BEN GHÉZALA.
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