Kickmeier-Rust, M. D., Albert, D., & Roth, R. (2007). A Methodological Approach to Address Individual Factors and Gender Differences in Adaptive eLearning. In K. Siebenhandl, M. Wagner, & S. Zauchner (Eds.), Gender in E-Learning and Educational Games: A Reader (pp. 71-84). Innsbruck: Studienverlag.
A Methodological Approach to Address Individual Factors and Gender Differences in Adaptive eLearning Michael D. Kickmeier-Rust, Dietrich Albert, Roswith Roth Department of Psychology, University of Graz, Austria
Key words: Gender-Differences, Adaptive eLearning, Knowledge Space Theory, Competence Performance Approach Abstract: Adaptive eLearning systems aim at personalizing the learning objects and test items according to a learner’s specific needs. There is a variety of existing adaptive eLearning systems, either research prototypes or systems in commercial use. However, these systems are primarily limited to adapt to learners’ current knowledge or to certain test items learners are capable of solving. Thus, in the past such approaches to adaptive eLearning have been criticized for the lack of adapting to individual factor and gender based-differences. In the current article we introduce the Competence Performance Approach, as an extension of Knowledge Space Theory, and propose the same methodology as basis for modelling individual factors and gender-based differences.
1 Adaptive approaches to eLearning Adaptive eLearning systems are used to tailor learners’ views of learning objects to their personal requirements. Such technologies are often incorporated to guide through a large body of learning objects assisting learners in their comprehension of that material. For example, an adaptive system may only provide learning objects which are suitable for the learner. Too difficult and also too easy learning objects might not be displayed in order to avoid visual and cognitive load and to suggest an appropriate learning path through the learning content. Existing adaptive eLearning systems such as ALEKS (www.aleks.com), ELM-ART [1], or KBS Hyperbook [2] demonstrated benefits for learners in terms of navigating through contents [3] and in terms of classroom and platform independence [4]. [5] found advantages of JointZone, an adaptive eLearning system in medical education, in terms of acceptance and also learning performance. Generally, these adaptive approaches attempt to compete the one-fits-all approach of traditional eLearning [6], accounting for certain requirements and preferences of a learner. Primarily, such approaches provide adaptive navigation and adaptive presentation of contents ([7], [8], [9], [10]). Adaptive navigation, as already mentioned, refers to guidance through learning objects by, for example, a customized hyperlink structure or format. The degree of freedom granted within a system is determined by a specific underlying learner model. Adaptive presentation refers to a customized presentation of learning objects. On the one hand this might refer to the visual or auditory design; on the other hand this might refer to the amount or grade of details of presented learning contents. However, adaptive eLearning systems have also been criticized and discussed regarding deficits and weaknesses. A major point is that almost all existing eLearning systems do not account for gender differences or individual factors such as personal abilities, preferences, learning styles, or learning strategies. This is remarkable since research on individual differences and gender emphasize possible gender-bias (e.g., androcentricity/gynocentricity, overgeneralization, gender insensitivity, or double standards; cf. [11]), which is influencing research and design. Moreover, individual abilities, needs, and preferences (e.g., different cognitive abilities, different learning styles and strategies, different
motivational and emotional states, different cultural influences, or different gender-based influences) are not sufficiently considered in existing adaptive approaches to eLearning. A reason might be that the diversity of learners makes it difficult for adaptive or intelligent systems to meet all these requirements. Schulmeister [12] formulates three major points of critique: (a) The number of influencing factors and their interactions are too high to be covered by an adaptive system. (b) Empirical research is lacking, which offers a sound basis to link learner factors with appropriate instructional design. (c) The gap between theoretical assumptions and “decisions” of an adaptive system cannot be logically deduced but depend on (human) values and judgements.
2 The KST framework In the current paper we propose a methodological approach that addresses these points of critique. This approach intends to provide a framework to operate not only (a) with relations among skills and competencies and their connections to observable behavior but also (b) with the variety of individual and gender-based variables related to the acceptance of an eLearning system and the learning success with that system. A theoretical framework, which was successfully applied to realize eLearning systems adapting to learner’s knowledge is Knowledge Space Theory. In a first step we give a brief introduction to this framework. In a second step we introduce an approach that uses the same framework to adapt to individual and gender-based factors.
2.1
Adapting to a learner’s knowledge state
A well-elaborated theoretical framework to address relations among knowledge, skills, and competencies is Knowledge Space Theory (KST) established by Doignon & Falmagne [13], [14]. It provides a set-theoretic framework for organizing a domain of knowledge (e.g., biology, chemistry, or mathematics) and for representing the knowledge based on prerequisite relations. For a finite set of problems Q = {p, q, r, s, ...} (e.g., test items) the prerequisite relation ° is a binary relation. The term p ° q states that a learner who is capable of solving item q is also capable of solving item p. In other words, we can assume that solving item p is a prerequisite of solving item q. The knowledge state of a specific learner is described by a subset of a set of problems (e.g. test items) related to a given domain this learner is able to master. Due to prerequisite relations among the problems of a domain, not all subsets of problems are possible knowledge states. To give an example, image five problems of the domain of basic algebra, an addition, a subtraction, a multiplication, a division, and an equation. For five problems the set of all possible knowledge
Figure 1: Upward drawing (Hasse graph) of a surmise relation (left) and a surmise function (right). Note that the bent line below item s on the right denotes an and/or relation meaning that either item p or item q is sufficient to master item s.
states is 25; if we assume that addition, subtraction, multiplication, and division are prerequisites for solving equations, not all 32 knowledge states will occur. For example, it is highly unlikely that a student will be able to solve equations but not addition problems. The collection of possible knowledge states corresponding to a prerequisite relation is called a knowledge structure. To account for the fact, that a problem may be solved in different ways and thus may be associated to different sets of prerequisites, the notion of a prerequisite function has been introduced [14], [15]. The surmise function σ ( p) , as a generalization of a prerequisite relation, associates a family of subsets of the domain with each problem (Figure 1). This family of subsets represents all possible ways to acquire mastery of a certain problem. In its original formalization, KST is rather behavioral, focusing on the observable performance without referring to the latent competencies underlying that performance. Thus, there have been attempts to extent the original basic framework (cf. [16], [17], [18], [19]). One extension, which incorporates explicit reference to the competencies that are required for mastering the problems of a domain, is the Competence-Performance Approach (CPA) by Korossy [20], [21]. The basic idea of CPA is to assume a basic set of abstract, latent skills or competencies that are relevant for mastering the problems of a domain. For each problem p ∈ Q exists a subset f ( p) ⊆ S of skills or elementary competencies that are relevant for mastering the problem. As in KST, on the set of skills a prerequisite relation a ° b or a prerequisite function σ (a ) can be established meaning that skill a is a prerequisite of skill b. The competence state of an individual is the collection of all available competencies of that person, which is not directly observable but can be uncovered on the basis of the observable performance on the problems representing the domain. As in KST, prerequisite relations or prerequisite functions are described on the set of competencies establishing a competence structure (Figure 2), which contains all possible competence states. By utilizing an interpretation function a set of skills required to solve a problem is assigned to each of the problems in Q. Vice versa, by utilizing a representation function to each competence state a set of problems is assigned, which can be mastered in the given competence state. Due to
Figure 2: The left part shows a surmise relation among skills a to f. The right part shows the competence structure resulting from the surmise relation. The bold lines of the competence structure indicate one of several possible learning paths.
these functions no one-to-one mapping between skills and problems is required. Figure 3 shows an example for mapping a set of learning objects or test items respectively to a competence structure. The outlined approach entails several advantages. Given the performance, the underlying latent skills can be identified. Due to the utilization of representation and interpretation functions no one-to-one mapping of performance (e.g., the responses to test items) to skills is required. Moreover, on the basis of a learner’s competence state the following meaningful learning objects can be identified. By the example of Figure 3, if a learner is in the competence state {a, b, c, e}, the next appropriate learning object must cover skill d, i.e. learning object X1. On this basis an adaptive eLearning system can provide a learner with efficient individual learning paths (Figure 2, right). Besides presenting adequate learning objects for a given competence state, the same principle can be used for an efficient adaptive assessment of the current competence state. There are adaptive assessment algorithms ([17], [22], [23], [24]), which are based on the principle that if a ° b holds and a learner is able to solve a test item that requires skill b, it’s not necessary to test skill a because it is a prerequisite of skill b.
Figure 3: Using an interpretation function to map learning objects or test items to a competence structure.
The approaches of KST and CPA were the basis for advanced research prototypes of adaptive eLearning prototypes such as APeLS (http://css.uni-graz.at/demos/apels) or RATH (http://css.uni-graz.at/rath), as well as the successful commercial eLearning platform ALEKS (http://www.aleks.com). Moreover, these formal, computational approaches contribute to the development of current state-of-the-art eLearning projects under EC’s IST framework (e.g., EleGI, iCLASS, or ELEKTRA). Concerning the focus on navigation and presentation the outlined approach is in tradition with other adaptive or intelligent eLearning systems. The same methodology, however, can be extended and utilized to create a learner model of individual abilities and preferences, as well as gender differences.
2.2
Adapting to individual factors and gender-based differences
On the basis of CPA, we propose a methodology that allows to model - equally as described above - individual abilities, preferences and gender-based differences. As for skills and test problems were established prerequisite relations for adapting to a learner’s knowledge, we propose to establish relations among individual factors (such as gender differences, learning styles, or cognitive abilities). Equally to skills, individual abilities or preferences are latent qualities that cannot be observed directly but assessed using certain test items. Thus we
refer to individual factors as a finite, non-empty set F of factors, e.g. learning styles, learning strategies, verbal and spatial abilities. Because probably no “necessary prerequisites” can be found between individual factors, meaning the degree of one characteristic determines necessarily the degree of another one, we utilize a surmise relation between those individual factors. Due to such relation we can assume from the degree of one characteristic the degree of another one. As examples, from being male we can surmise that it is unlikely that this person has a pragmatic learning style [25] or from having a visual learning style we can surmise that this person has preferences for pictorial learning objects. There is a large body of empirical results that serve the development of such surmise relations. For example, dependencies of gender and learning styles were reported by Logan & Thomas [25]; dependencies between learning styles and learning strategies were reported by Oxford [26]. Hall & Hickman [27] demonstrated gender differences in visual input preferences, or Li [28] yielded dependencies between gender and interactivity in web-based learning environments. The selection of individual factors that should be integrated in this learner model relies on psychologists, pedagogues, and designers. Of course, they must be based on the existing body of empirical research. When aiming for gender-fair eLearning, also different approaches to computers in general must be considered. Dickhäuser and Stiensmeier-Pelster [29] described a model that incorporated individual factors influencing a learner’s choice to use a computer for educational purposes. These authors describe factors like: • attribution index (the causes individuals attribute their success or failure to internal, i.e. personal abilities, or external, i.e. computer error, factors) • self-concept (individual judgment about personal capabilities using a computer) • frequency of computer usage • value of computer usage (individual judgment of the value of working with computers) • expectation of using a computer successfully. These factors interdependently contribute to the individual choice of using a computer. The model describes conditional dependencies among these factors, meaning that the degree of one factor is influencing the degree of another. The degree of these factors differs significantly between males and females [30]. With regard to adaptive eLearning this means that the described factors influence learners’ expectations regarding their success of learning with computers, their motivational states, and, most importantly, their choice of using an eLearning platform for learning. Melis & Ulrich [30] proposed an extension of this model based on a Bayesian Net (see Figure 4). This model includes additional behavioral and mental factors like the situational goal, user experience, accepting of help, need of re-assurance, and amount of exploration. These authors also describe the consequences for implementing this model to adaptive eLearning systems; for example, personalizing presentations of error messages to take influence on individual failure attribution or an adaptive raise of success expectation. The Bayesian Net model [30] is shown in Figure 4 (the original model is marked by gray circles). We extended this model by additional factors, such as gender, motivational components, learning styles, learning strategies, and input preferences (according to individual learning styles). We also included a pedagogical model which serves the adaptive selection of learning objects or test items respectively. This model allows to extract certain factors and to establish a learner structure. This selection might be influenced by pedagogical decisions, the availability of empirical findings as basis for conditional dependencies, or the availability of test items to assess such factors. Subsequently, the learner state of each learner can be determined. According to the pedagogical model (e.g., 8LEM [31]) on this basis the adaptive system can select the most appropriate learning object or test item for a specific learner. This selection, of course, depends on the number of available learning objects or test items. Very likely, not for all learner states a related learning object or test item will be available. But the described
Figure 4: Bayesian Net model of Melis & Ulrich [30] and its extension. Gray fields indicate the elements of the original model.
methodology allows selecting the most appropriate learning object or test item from the pool of available ones. The major advantage is, as mentioned above, that the described methodology allows reducing the number of possible learner states (i.e. combinations of factors). On the basis of this extended model (Figure 4) and the existing body of empirical results in this domain (cf. [32]) we can establish a surmise relation or surmise function between such factors and their degree. Once, the surmise function is established we can create the corresponding (let’s call it) learner structure. A major advantage of this approach is that due to the relations among individual factors the number of possible learner states can be significantly reduced. By above (very simple) example concerning gender and pragmatic learning style, if we can surmise from being female to have a pragmatic learning style, we just receive two learner states, one for females and one for males, instead of two states for males and two for females. When considering a large number of individual factors and gender-based differences and, moreover, a large number of different degrees of different characteristics, this reduction of possible combinations might be a crucial point for the feasibility of real-time computing, e.g. within an adaptive eLearning system. The described structure can be mapped to a set of learning objects or test items, utilizing interpretation and representation functions. Again no one-to-one mapping is necessary. An adaptive system might start with easy assessable factors such as gender; and make preliminary assumptions about which types of learning objects might be the most successful for a certain type of learner. Due to initial diagnosis of further factors or due to a learner’s preferences, actions, and learning success recorded in the process of a learning episode, the systems can update the learner profile not only regarding the knowledge state but also regarding individual factors, and approach a more refined perspective of the learner. This assures the presentation of learning objects suitable for a specific learner.
2.3
Combining both approaches
To combine both, adapting to a learner’s competence state and to its learner state we need a methodology that allows formalizing both structures in a computable way. A promising method is the use of ontologies. An ontology in computer science is a data model to represent entities of a domain and relations among them. An ontology includes concepts or classes which are generic terms, e.g. learning objects or test items, and it includes instances or individuals related to a set of concepts, e.g. a specific test item. The entities of an ontology can be described with attributes. Each attribute has a name, a certain data type, and one or more values, e.g. attribute "language" and value "English". Attributes, moreover, serve to establish relations among entities. For example, an relation might be "A is part of B". The prerequisite and surmise relation introduced in this article can also be modelled in this way. Thus, a skill (on the latent level) or a learning object / test item (on the performance level) may have an attribute “is prerequisite of". Ontologies allow for a formalized and computable representation of a domain and the related entities. Generally, this is accomplished with XML metadata. A typical standard is OWL (ontology web language). In the framework of the ELEKRA (www.elektra-project.org) project currently attempts are made to formalize a comprehensive learner model by OWL repositories. The aim of ELEKTRA is to merge the advantages of computer games (e.g., immersion, motivation, perseverance) with learning goals. ELEKTRA will develop an innovative design and development methodology for producing e-learning experiences based on computer games. This methodology will be derived from combining research in cognitive science, pedagogical theory and neuroscience with best industrial practice in computer game design and elearning software design. As a demonstrator ELEKTRA will produce a 3D virtual reality based virtual learning environment, in which learners can experience learning experiences as rich as gaming experiences. These enhanced learning experiences will improve the knowledge transfer of the learner through innovative knowledge representation and visualisation: The learners will be able to actively interact with and visualise the relationships between concepts and engage in multimodal approach of concepts. The anticipated outcome of ELEKTRA is new approach to design and development of eLearning experiences that is underpinned and supported by research and evaluation findings.
3 Gender-fair design To draw a complete picture, gender-sensitive approaches to eLearning not only require adapting to possible gender-based differences or individual abilities and preferences, as very basis, the design of learning objects and test items must be gender-sensitive and gender-fair. As suggested by Roth (2002), the described diagnostic process must consider gender-based issues like a bias in the measured constructs, in the used methods, and the utilized items (cf. [33], [34], [35], [36]). Androcentricity and gynocentricity are the basis for such considerations. Androcentricity means a focus on characteristics of males and on male norms. Generally, this is the dominant approach in our culture. Male behaviour and capabilities serve as the standard and are considered as “normal” [33]. In contrast, gynocentricity means a view of the world from a female perspective. In our culture this perspective often is less positive valued than the male perspective. According to [11] a bias towards female attribution is given in clinical psychology, where disorders (e.g., depression or eating disorders) are more likely attributed to females. Additional bias comes from an overgeneralization from one sex to another. This means that characteristics, abilities, or preferences are seen as if they were applicable to both females and males to the same extent. According to Addams & Ware [37] the most problems with regard to sexist or gender-biased language are based on overgeneralization. A similar source of gender bias come form gender insensitivity. In many cases gender is not considered to be an important social category influencing, for example, design processes.
Whilst the described source of gender-bias concern a more general perspective, designers of learning objects and test items must be aware that there are further factors which directly influence the design of environment, learning objects, and test items. (a) Construct bias: Based on general sources of gender-bias often certain constructs are considered to be an equal construct across males and females. For example, the construct of self-esteem has different meanings for males and females. As some studies ([11], [38][39]) have shown, for males high self-esteem means for example assertiveness, eloquence, leadership, or high social status, while for females high self-esteem means rather being emotionally balanced, tolerant, liberal, or using adequate coping strategies. (b) Method bias: Test items require the use of methods which are adequate for both, males and females [33]. Differences in familiarity with and novelty of targets, test exposure and test instruments of males and females must be considered. (c) Item bias: Also on the item-level designers must consider the use of test items which are gender-fair. For example, being “over-assertive” was considered as positive by males and rather negative by females [11], [36].
4 Conclusion We have presented a methodological approach, which is already successfully applied as basis for eLearning systems adapting to a learner’s knowledge. In parallel, we propose to utilize the same methodology to establish a learner model accounting for individual factors and gender differences. This methodology has several advantages: (a) It allows a theorydriven and empiric creation of surmise relations among individual factors. (b) The established surmise relations and the related learner structure can serve as hypotheses for empirical validations. (c) The set-theoretic, formal nature of the approach allows to provide adaptive eLearning systems with computable and computer-understandable representation of the learner model, e.g. in form of XML repositories or ontologies. Furthermore, this approach addresses major points of critique on existing adaptive approaches [12]: (a) The vast number of individual factors influencing learning preferences and learning factors can be reduced by a meaningful selection of factors, which have been empirically proved to be influencing learning. Additionally, by establishing surmise relations between these factors the amount of possible learner states is reduced further on because specific combinations of factors are unlikely. (b) The approach allows relying on an existing empirical basis regarding individual factors and their interrelations. A major advantage is that, because no one-to-one mapping is required between individual learner states and the assigned learning objects, a change in the surmise relation and thus in the learner structure (e.g. due to new empirical findings) does not necessarily require a change in the learning objects but only an adaptation of representation and interpretation functions. (c) The approach does not require the adaptive system to make inferences from a learner state to a presentation of certain learning objects. Such decisions rely on psychological and pedagogical guidelines, which are modelled in the representation and interpretation functions. Future research and development will investigate the introduced methodological approach more in-depth and will incorporate it into adaptive eLearning systems. Of course, future research must also intensify investigations regarding the factors influencing learning success and, above all, regarding the interrelations and dependencies among these factors, in order to broaden the empirical basis for the described methodology.
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Authors: Michael D. Kickmeier-Rust, Mag.rer.nat. Cognitive Science Section, Dep. of Psychology, University of Graz, Austria Universitätsplatz 2 / III, A-8010 Graz +43-316-380-8549,
[email protected] Dietrich Albert, Univ.-Prof. Dr. Cognitive Science Section, Dep. of Psychology, University of Graz, Austria Universitätsplatz 2 / III, A-8010 Graz +43-316-380-5118,
[email protected] Roswith Roth, Univ.-Prof. Dr. Clinical and Health Psychology Section, Dep. of Psychology, University of Graz, Austria Universitätsplatz 2 / III, A-8010 Graz +43-316-380-5127,
[email protected]
Authors short biographies: Michael D. Kickmeier-Rust is a psychologist with major interests in computational psychology, human-computer interaction and eLearning. He is postgraduate researcher at the Cognitive Science Section, Department of Psychology, University of Graz, member of the ACM, and vice-chair of the Austrian Aviation Psychology Association. Currently he is working on his dissertation related to game-based learning in the framework of the IST-project ELEKTRA. Dietrich Albert is Professor of Psychology at the Department of Psychology at the University of Graz and he is the head of the Cognitive Science Section (CSS). His major interests are technology enhanced learning and teaching. He is a member of various scientific societies and advisory boards. More information is available at http://css.uni-graz.at. Roswith Roth is Professor of Psychology at the Clinical and Health Psychology Section. She is head of the working group of health and gender psychology and head of the committee of equal opportunity. Her main research interests are in health psychology, gender psychology, gender equality in higher education and gender-fair research designs.
