Optimization Theory, Methods, and Applications in Engineering 2013
View this Special IssueResearch Article  Open Access
Building a Smart EPortfolio Platform for Optimal ELearning Objects Acquisition
Abstract
In modern education, an eportfolio platform helps students in acquiring elearning objects in a learning activity. Quality is an important consideration in evaluating the desirable elearning object. Finding a means of determining a high quality elearning object from a large number of candidate elearning objects is an important requirement. To assist student learning in a modern eportfolio platform, this work proposed an optimal selection approach determining a reasonable elearning object from various candidate elearning objects. An optimal selection approach which uses advanced information techniques is proposed. Each elearning object undergoes a formalization process. An Information Retrieval (IR) technique extracts and analyses key concepts from the student’s previous learning contexts. A contextbased utility model computes the expected utility values of various elearning objects based on the extracted key concepts. The expected utility values of elearning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate elearning objects. The main contribution of this work is the demonstration of an effective elearning object selection method which is easy to implement within an eportfolio platform and which makes it smarter.
1. Introduction
Effective learning is always a key consideration in compulsory education. Acquiring high quality elearning objects to assist a student in a learning activity is an important requirement. Eportfolio is a modern platform constructed for students in an educational environment [1, 2]. In a comprehensive learning activity, students record learning context information, such as credits, score, and reports, in an eportfolio platform. In addition, an eportfolio platform helps students in acquiring official elearning objects, that is, slides, exercises, videos, voice files, and so forth. Quality is an important consideration in evaluating the desirable elearning object for a specific learning activity. A smart eportfolio platform is required to support high quality elearning objects for students’ effective learning [3–5].
In a learning activity, student feedback on an evaluation process can be represented as a utility model [6] reflecting the satisfaction a student obtains from choosing a reasonable elearning object. The student provides a utility model before committing to an elearning object; context information [7] in the learning activity provides rich clues for elearning object acquisition. Based on the context information of a learning activity, uncovering hidden knowledge is important. Therefore, context information analysis can quantify all the influences of the various factors and their relationships in order to consolidate a utility model [8]. The student’s contextbased utility model can be applied to monitor context information, in order to evaluate the elearning object’s quality. The student will obtain the expected utility value of the issue of interest when choosing an elearning object.
Because there are various issues of interest, selecting a reasonable elearning object from a large number of candidate elearning objects requires a multicriteria decision analysis. A multicriteria decision analysis is concerned with structuring and solving decision and planning problems involving multiple criteria [9]. Each specific issue of interest in the contextbased utility model is considered as a criterion. The expected utility value of the issue of interest is shown as the criterion’s evaluating value. According to a learning activity and students’ learning context, several criteria’s evaluating values need to be analyzed in order to determine which elearning object is a reasonable one.
This paper explores the context of a learning activity and uses a selection approach for candidate elearning objects in order to assist the student in acquiring a reasonable elearning object. First, each elearning object undergoes a formalization process. Moreover, the proposed system employs Information Retrieval (IR) techniques to extract the key concepts of relevant information necessary to handle a specific learning activity. The extracted key concepts form a learning activity profile that models the information needs of students for handling elearning objects in certain contexts. A contextbased utility model explores the learning activity’s context information in order to obtain the candidate elearning objects’ actual expected utility values. Then, a multicriteria decision analysis uses the actual expected utility values to determine the optimal selection order of the candidate elearning objects. A fuzzy weight model is used to consolidate the multicriteria decision analysis method. Finally, the selection order is considered as reasonable decisionmaking knowledge for the student to optimally select a reasonable elearning object. In this paper, an experiment is conducted to demonstrate that the selection approach is effective. The main contribution of this work is the demonstration of an effective solutionselecting method which is easy to implement, in order to build a smart eportfolio platform.
The remainder of this paper is organized as follows. Section 2 reviews related works on eportfolio and learning contexts, the contextbased utility model, and multicriteria decision analysis. Section 3 introduces the proposed optimal elearning object selection method for building a smart eportfolio platform. Section 4 uses a specific case to illustrate the steps of an optimal selection approach. The prototype eportfolio platform, experiments, and relevant discussions are shown in Section 5. Finally, in Section 6, conclusions are presented.
2. Related Works
The related literature covers the eportfolio platform and learning contexts, the contextbased utility model, and multicriteria decision analysis techniques.
2.1. EPortfolio Platform and Learning Context
Originally, portfolios presented the best works of literature and art as evidence for showing job and personal achievements. Until 1980, it was used in the education domain and transformed into digital format eportfolio by Information Technology, for example, voice, image, text, and multimedia; it was not restricted by computer media type. In modern education, the eportfolio platform is built for students in an educational environment. Students construct and access personal eportfolios in the eportfolio platform to review their selflearning processes [1, 2]. An eportfolio platform also assists the teacher in providing a modified teaching model for the student to facilitate effective learning [10, 11]. A smart eportfolio platform will facilitate student acquisition of high quality elearning objects [3–5].
According to the definitions [7], context includes the location of the user, the people’s identities, and objects around the user, and the devices interact with the user. In other words, context is any information that characterizes the situation of an entity, where the entity can be a user, place, service, or service relevant objects [12]. A learning activity is what a student does in terms of learning in a specific domain during a period of time. A learning activity is considered an entity; we can characterize its relevant context information, including and the environments, credits, scores, reports, and official and comprehensive elearning objects. By tracking a learning activity, a student’s learning context provides rich clues for object selection. The learning context is composed of a series of learning tasks. This may involve several semesters and academic years. The student’s learning context may include not only official tasks (e.g., courses) but also comprehensive tasks (e.g., practical training, license testing, and science research tasks). These different learning tasks enrich a student’s learning context [8]. Therefore, constructing a smart eportfolio platform for determining a reasonable elearning object that will enhance effective student learning is a modern educational trend [13–16].
2.2. ContextBased Utility Model
Utility function is one kind of multiattribute utility theory which helps users to solve a multicriteria complex problem by utility analysis processing for decision making. Some researchers use utility theory to create various information systems. A decisionmaking system has been proposed based on utility theory to increase the precision of decisions [17]. A bidirection auction mechanism has been proposed which used utility function to predict user behavior in the auction process [18]. A utilitybased model has built for serviceoriented computing [6]. In addition, quality is an important consideration in evaluating a problem’s solution. Worker feedback on an evaluating process can be represented as a utility model reflecting the satisfaction a worker derives from choosing a solution. The worker provides such a utility model before committing to using a solution [19].
In a comprehensive learning activity, students record learning context information in an eportfolio platform. Context information in a learning activity provides rich clues for elearning object selection. Based on the context information of a learning activity, uncovering hidden knowledge is important. Some researches use context information to infer more knowledge to assist users in solving problems. Therefore, context information analysis can quantify all of the influences of the various factors and their relationships to consolidate a utility model. The student’s contextbased utility model can be applied to monitor context information in order to evaluate the elearning object’s quality. The student will obtain the expected utility value of the issue of interest when choosing an elearning object.
2.3. Multicriteria Decision Analysis
Typically, a unique optimal solution does not exist for such problems, so it is necessary to use the preferences of the decisionmaker to differentiate between solutions. Multicriteria decision making (MCDM) has played an important role in solving multidimensional and complicated problems [20, 21]. The purpose is to support decisionmakers facing such problems. Therefore, optimal methods are used to enforce multicriteria decision analysis, that is, TOPSIS, VIKOR, and ELECTRE. TOPSIS, VIKOR, and ELECTRE methods have been used to prioritize the production lines [22]. ELECTRE method has been applied with seven criteria for selecting the best one amongst five personnel and identifying the personnel [23]. A selection approach has been proposed for optimized web services compositions based on an ELECTRE method [24]. The ELECTRE methods have been used in optimal problemsolving process for selecting a reasonable solution [25]. The ELECTRE methods have been used in optimal message negotiation process for selecting a reasonable solution in the eservice environment [26]. The ELECTRE methods haves been enforced to the multiattribute decision making under risk with interval [27].
Elimination Et Choice Translating Reality (ELECTRE) is a family of multicriteria decision analysis methods. ELECTRE methods include two main stages. In the first stage, the method constructs the outranking relationships for a comprehensive comparison of each pair of actions. In the second stage, the method elaborates on the recommendations based on the results obtained by an exploitation procedure in the first stage. The nature of the recommendations depends on the problems: choosing, ranking, or sorting [9]. This paper proposes a modified version of the ELECTRE method to determine the optimal selection order of candidate elearning objects. The selection order is presented to the student to determine which elearning object is a reasonable elearning object chosen from candidate elearning objects.
3. The Proposed Approach for Optimal ELearning Object Acquisition
In this section, a selection approach using a modified version of the ELECTRE method [24–26] for candidate elearning objects is described in terms of a contextbased utility computing and fuzzy weighted multicriteria decision analysis. The approach includes the use of elearning object formalization, contextbased utility computing for candidate elearning objects, and the selection order discovery of candidate elearning objects, as shown in Figure 1.
3.1. ELearning Object Formalization
ELearning object formalization is the essential task of the selection approach. This paper refers to a utilitybased reputation model [6, 25, 26] to formalize elearning object quality factors in order to reinforce the contextbased utility computing.
Let denote the set of elearning object, and . Let denote the set of elearning object providers, , and lets function denote the elearning objects provided by an elearning object provider, where represents the power set operator. Let denote the set of students in the system, and . Each elearning object has associated issues of interest, denoted by set , which students are interested in monitoring, and . Function IS represents the set of issues of interest for an elearning object: . Function denotes the expectations of a student for the elearning objects undertaken, where denotes the real numbers. Notation represents the expectations of student on issue concerning the elearning object supplied by provider . In a learning activity, a potential issue of interest could be the quality of the elearning object. A smart eportfolio platform can develop a contextbased utility model which reflects the satisfaction which students gain from choosing an elearning object.
3.2. ContextBased Utility Computing for Candidate ELearning Objects
After the expectation formalization process of an elearning object’s specific interest issue, a contextbased utility model is developed to represent student satisfaction with the elearning object acquisition.
The elearning object’s context attributes are key concepts extracted from the learning context of a specific learning activity by Information Retrieval (IR) technology [19]. The extracted context attributes are assumed to be the learning context information. The extracted context attribute, mapped to a specific interest issue of an elearning object, is used as a quality factor to build a reference case . is set as a desired elearning object with expected utility values for specific interest issues. The relevant context attributes of thecandidate elearning object form a comparative case . The similarity value of the two cases, and , is defined in (1), as derived according to their values of context attribute ; value denotes the transformed value of context attribute of , which is calculated by the discretization process: The similarity function used to compute the similarity measured between cases and is defined in (2) where is the similarity value obtained from the values of context attribute and is the weight given to context attribute . Note that the total of all is equal to 1. If value is closer to 1, it means that and have a high correlation. If value is closer to 0, it means that and have a low correlation.
Let ; let denote the utility that student gets by obtaining the actual value on issue from elearning object of provider . Utilities are normalized and scaled to . Based on various issues of interest, selecting the best elearning object from a large number of elearning object requires multicriteria decision analysis.
3.3. Determining a Selection Order of Candidate ELearning Objects
For the second task, this paper proposes a modified version of the ELECTRE method [24–26] to determine the selection order for candidate elearning objects. If there are candidate elearning objects which involve quality factors, the matrix of expected values can be shown as in (3). The modified version of the ELECTRE method is used to determine the optimal selection order of an elearning object. The decision matrix is a normalization matrix from the elearning object normalization process described in Sections 3.1 and 3.2: To calculate the weighted normalization decision matrix, a weight for each quality factor must be set to form a weighted matrix (). The weighted matrix is dealt with by fuzzy method: center average defuzzifier (CAD). For a weight value set , is the weight value of a specific elearning object utility and is the total count. Equations (4) get the fuzzy fragment value, is the center of fuzzy sets, and is its height: The multiplication of a normalization matrix by a weighted matrix then obtains the weighted normalization decision matrix , as Compare arbitrarily different row and row in the weighted normalization decision matrix to verify the concordance and discordance set. If value of row is higher than value of row , the component can be classified as the concordance set , or the discordance set . The sum of each component’s weight forms a concordance matrix , as A discordance matrix can be presented as ; we use a formula to get the discordance matrix. is the set including all quality factors, , as The reverse complementary value is used to modify to obtain the modified discordance matrix . To show the large component value of the candidate elearning object, when the expected value is larger, we combine each component of the concordance set with the modified discordance matrix in order to calculate the production and get the modified total matrix (, Hadamard product of and ). We obtain the maximum value of each column from the modified total matrix. The purpose is to determine the modified superiority matrix. To make a reasonable elearning object, we have to rank from small to large: . The threshold is set behind the smallest value and the next smallest value . If the value is smaller than threshold , it is replaced as 0 or 1. We then get the modified total superiority matrix, as Finally, the matrix indicates that elearning object is better than elearning object . We can eliminate elearning object and show it as .
The relationships between the quality factors of the candidate elearning objects as well as the optimal selection order for all candidate elearning objects are obtained. The candidate elearning object is the solution provided by the elearning objects provider. The student can follow the selection order to obtain a reasonable elearning object.
4. A Use Case to Illustrate the Steps of an Optimal Selection Approach
This section presents the use of a specific case to illustrate the steps of the proposed optimal selection approach.
4.1. ELearning Objects Formalization and ContextBased Utility Model
When a student engages in a specific learning activity, various suppliers provide elearning objects. We use elearning object formalization and a contextbased utility model to precompute a student’s expected list of supplied elearning object quality factors and to facilitate a multicriteria decision analysis to discover an optimal selection order for candidate elearning objects.
First, the elearning object formalization process identifies the student, elearning object, and elearning object providers. Then, the student can choose the indicators (quality factors) of the current learning activity. We use a practical project learning activity as a simple use case process. The student sets Introduce, Practice, and Testing as the quality factors for the practical project learning activity. Then, the relevant values of the quality factors and elearning objects are recorded in a table, as shown in Table 1. The elearning objects A, B, and C are used as the candidate elearning objects for the demonstration of the proposed method in these experiments. For example, elearning object A sets the quality factor, the practical project learning activity, where the Introducing degree is high, Practicing is middle, and Testing is evaluated as low.

After the elearning object formalization process, a contextbased utility model is developed to represent student satisfaction with the elearning object acquisition. Each quality factor is normalized, and scaled to . Table 1 is then transformed into Table 2.

4.2. The Selection Order Discovery of Candidate ELearning Objects
This work proposes a modified version of the ELECTRE method [24–26] to discover the optimal selection order of candidate elearning objects for a specific learning activity. The decision matrix of expected values can be shown as follows: The fuzzy weighted matrix for each quality factor is shown as follows: The multiplication of a normalization matrix and a weighted matrix produces the weighted normalization decision matrix , as follows: The concordance set or the discordance set is shown as follows: The sum of each component’s weight forms a concordance matrix : A discordance matrix can be presented as : A modified discordance matrix can be presented as : A modified total matrix can be presented as : A modified total superiority matrix is shown as : Finally, we get the optimal selection order for all candidate elearning objects. The experiment results show that elearning object is better than elearning object ; elearning object is better than elearning object ; and elearning object is better than elearning object . The student can follow the optimal selection order to obtain a reasonable elearning object.
5. Experiments and Discussions
This section demonstrates the prototype eportfolio platform and presents the experiment results and relevant discussion.
5.1. The Prototype EPortfolio Platform
The prototype eportfolio platform [13] is shown in Figure 2. The system framework comprises the learning context, learning activity context rule discovery, learning activity profile discovery, and knowledge recommendation modules. The learning context module gathered runtime information of a student’s learning activities, such as learning features and context information. According to the identified learning context and contextknowledge view, based on the knowledge recommendation module, the system evaluated the student’s learning status and recommended relevant knowledge documents. The specific learning context, including learning activities and corresponding knowledge documents, was recorded in the records of the eportfolio.
We used the log file in a prototype eportfolio platform [13] as a source of analysis data. For specific learning activities, relevant elearning objects accessed by students are recorded in the prototype eportfolio platform log. Information Retrieval (IR) technology is used to extract the key concepts of elearning objects based on a learning context of a learning activity. The extracted key concepts form a learning profile, which is used to model the information needs of the students. We assume that a generic learning activity is specified by experts. Different students may find different elearning objects for the same learning activity, according to their abilities. The prototype eportfolio platform log records historical learning context instances.
5.2. Experiment
The experiments on the practical project learning activity in a university [13] are shown in this section. The prototype eportfolio platform log was used as a source of analysis data. This paper used Information Retrieval techniques to analyze the data, and 3,164 relevant data records were obtained from the practical project learning activity. The retrieved data records involve 8 learning activities, 41 students, and 813 elearning objects. In this research, four domain experts assisted in carrying out the experiments and the evaluation of the results. The experiment results from this paper’s method show that precision is 37.94% (96/253) and recall is 45.71% (96/210). The experiment results of the method proposed in the research [13] show that precision is 31.23% (79/253) and recall is 37.62% (79/210). The selection method used in this work seems to be more effective than the method proposed by [13].
5.3. Discussion
The lower values of precision and recall indicate that current elearning objects’ quality is not good enough to support the practical project learning activity. In the experiment process and results analysis, it was found that the practical project learning activity is a comprehensive learning activity. Students fetch various official and unofficial elearning objects to explore an open topic and find a comprehensive solution, that is, database theory, system programming, network protocol, project management, and so forth. The elearning objects provided by an eportfolio platform seem to be insufficient to assist effective student learning. In addition, the elearning object’s actual utility values from the contextbased utility model and the weight value in multicriteria decision analysis tasks are the critical factors influencing the experiment results. For example, the normalization utility values and weight values are indistinguishable. This prevents the method from identifying a reasonable elearning object for the elearning object determination. This study checks and adjusts the normalization utility values and uses a fuzzy weight model to enhance the distinguishing ability. User feedback influences how the quality factor is decided. The quality factor is the critical item for the contextbased utility model and the multicriteria decision analysis processing.
6. Conclusions
To assist student learning in a modern eportfolio platform, this work proposed an optimal selection approach determining a reasonable elearning object from various candidate elearning objects. Each elearning object has a formalization process. An Information Retrieval technique extracts and analyzes key concepts from the student’s previous learning contexts. A contextbased utility model computes the expected utility values of various elearning objects, based on the extracted key concepts. The expected utility values of elearning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate elearning objects. The selection order is presented as the decisionmaking knowledge to assist a student in acquiring a reasonable elearning object.
The experimental results demonstrate the effectiveness of providing decisionmaking knowledge to help students learn. The main contribution of this work is the demonstration of an effective elearning object selection method that is easy to implement into an eportfolio platform, making it smarter. Future studies can pay more attention to designing interactive feedback mechanisms. Feedback can enable the eportfolio platform to perform intelligent turning and learning in order to improve the proposed approach and make it more robust. Furthermore, the property of a heterogeneous learning environment should be considered in order to provide contextaware computing and ubiquitous learning support.
Acknowledgment
This research was supported in part by the National Science Council of Taiwan (Republic of China) with an NSC Grant 1022410H025017.
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Copyright
Copyright © 2013 ChihKun Ke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.