systems Article
A Knowledge Comparison Environment for Supporting Meaningful Learning of E-Book Users † Jingyun Wang 1, *, Hiroaki Ogata 2 and Atsushi Shimada 2 1 2
* †
Research Institute for Information Technology, Kyushu University, Fukuoka 812-8581, Japan Faculty of Arts and Science, Kyushu University, Fukuoka 819-0395, Japan;
[email protected] (H.O.);
[email protected] (A.S.) Correspondence:
[email protected] or
[email protected]; Tel.: +81-092-642-2297 This paper is an extended version of our paper published in the 23rd International Conference on Computers in Education (ICCE 2015), Hangzhou, China, 30 November–4 December 2015
Academic Editor: Jon Mason Received: 31 January 2016; Accepted: 28 April 2016; Published: 16 May 2016
Abstract: In this paper, we present an ontology-based visualization support system which can provide a meaningful learning environment to help e-book learners to effectively construct their knowledge frameworks. In this personalized visualization support system, learners are encouraged to actively locate new knowledge in their own knowledge framework and check the logical consistency of their ideas for clearing up misunderstandings; on the other hand, instructors will be able to decide the group distribution for collaborative learning activities based on the knowledge structure of learners. For facilitating those visualization supports, a method to semi-automatically construct a course-centered ontology to describe the required information in a map structure is presented. To automatically manipulate this course-centered ontology to provide visualization learning supports, a prototype system is designed and developed. Keywords: meaningful learning; critical thinking; knowledge structure; comparison skill; course-centered ontology
1. Introduction Nowadays, e-book systems are widely used together with Learning Management Systems in the education field. For example, in the Arts and Science Department of Kyushu University Faculty, not only Moodle but also the BookLooper e-book system developed by Kyocera Communication Systems is used for supporting daily classroom teaching. Those two systems provide a platform for instructors to easily organize and manage teaching materials; on the other hand, learners can conveniently browse the learning resources. Especially, in the BookLooper system learners can even mark text or add comments on any PDF files and the system can record their learning activities (including which pages they read and how they switch between pages) and report to instructors [1]. However, as other existing systems, neither BookLooper nor Moodle provide any function to support learners in constructing their knowledge framework effectively. When a learner acquires different knowledge items, she/he may also compare those items and understand some relations between them at the same time; those acquired knowledge items and their relations form the knowledge framework of the learner. Knowledge framework development usually is not supported in the older systems, and also, in those systems it is difficult to identify the relevant knowledge items that a learner possesses before and after a learning activity. Ausubel’s learning psychology theories [2–4] define the effective assimilation of new knowledge into an existing knowledge framework as the achievement of “meaningful learning”. Those theories suggest that knowledge finally gets incorporated into the human brain when organized in hierarchical Systems 2016, 4, 21; doi:10.3390/systems4020021
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frameworks and that learning approaches that facilitate this kind of organization significantly enhance the learning capability of all learners. In contrast, in the case of rote learning, knowledge tends to be quickly forgotten unless rehearsed repeatedly. Moreover, retained knowledge cannot contribute to enhance the learner's knowledge framework and has a low possibility of being used in future problem-solving [5]. However, how to move beyond rote learning and help learners to effectively construct their framework is still an open issue. In our previous research [6,7], the experimental results suggest that with the support of a system, which provides a visual environment to encourage the comparison of related “knowledge points” so as to foster meaningful learning, participants achieved significantly better learning achievement than those without the system support. In this research, a Knowledge Point (KP) is defined as “a minimum learning item which can independently describe the information of one certain piece of knowledge in a specific course”; a learner can understand a KP by its own expression or can acquire it by practice. As mentioned before, in our previous experiment we encouraged the students to use comparison in their learning procedure. However, what do we mean by comparison exactly? “Comparison” is an essential skill required by critical thinking which involves logical thinking and reasoning. This skill refers to estimating, measuring, or noting the similarity or dissimilarity between two or more objects. The human thinking skills “compare” and “contrast” appear in both “comprehension” and “analysis” levels within the cognitive domain (in total six identified levels: “knowledge”, “comprehension”, “application”, “analysis”, “synthesis”, and “evaluation”) according to Bloom’s taxonomy of intellectual behavior in learning, which contained three overlapping domains: the cognitive, psychomotor and affective domains [8]. From the educator’s perspective, knowledge comparisons could significantly support learner comprehension of the new KP [9–11]. However, the existing e-learning systems, which organize the knowledge of a curriculum in a tree structure based on the textbook chapters or the class schedule, usually do not support the effective construction of knowledge frameworks by encouraging knowledge comparison because they cannot characterize essential relations between KPs. For example, in a Moodle system there are two KPs, “d” and “t”, which are located in Lesson 1 and Lesson 10, respectively. In Lesson 10, to compare the KP “t” with the prior KP “d”, the instructor has to indicate the location “d” (this can be done with a hyperlink) and explain the relation between “d” and “t”. Even so, it is still difficult for the learners to locate the learning materials which directly address the relation between these two KPs, unless they look through all the learning material in Lessons 1 and 10. The searching will be even more time-consuming if the learner is comparing three or more KPs in a course at one time. To resolve these difficulties, an ontology-based Visualization Support System for e-book users (VSSE), which uses a hierarchical map structure to manage the KPs of a curriculum, was developed in this research to encourage the cultivation of comparison skills and foster meaningful learning. 2. Visualization Supports for both Learners and Instructors 2.1. A Visualization Support for Learners Using E-Books For the learners in an e-book system, normally in one activity, they read several pages of a file. As shown in Figure 1, after one preview activity (for example, studying pages 10–13, which cover seven new knowledge points) in BookLooper, the learner can log in to VSSE, the Visualization Support System for E-book users, to check the new knowledge points just studied. Our system will try to encourage the learner to understand the relations between the new KPs visually. Furthermore, the system also will make use of the quiz results of learners to identify the learner’s acquired KPs and then encourage the learner to compare the new KPs with related acquired ones visually. Finally, the system is also expected to recommend one KP or some KPs based on individual knowledge structure and guide learners to personalized learning paths. With this kind of visualization support, learners using BookLooper are expected to build up their knowledge framework more effectively.
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Figure 1. Visualization support for learners using e-books. Figure 1. Visualization support for learners using e-books.
Figure 1. Visualization support for learners using e-books. 2.2. A Visualization Support for Instructors of E-Book Users
2.2. A Visualization Support for Instructors of E-Book Users instructor Support of e-book we also intend Users to provide several visualization supports. Firstly, 2.2. AFor Visualization forusers, Instructors of E-Book the instructor can visually check all the learner’s knowledge (including preview statuses For instructor of e-book users, we also intend to providestructures several visualization supports. Firstly, For instructor of e-book users, we also intendthe to provide several visualization supports. Firstly, and quiz results) through our systems. Secondly, system will identify the most difficult KPs or the instructor can visually check all the learner’s knowledge structures (including preview statuses and the instructor can visually check all the learner’s knowledge structures (including preview statuses relations based on the frequently keywords and the willmost highlight torelations attract quiz results) through our most systems. Secondly,marked the system will identify difficultthem KPs or and quiz results) through our systems. Secondly, the system will identify the most difficult KPs or instructors’ attention. based on the most frequently marked keywords and will highlight them to attract instructors’ attention. relations based on the the most frequently marked keywords and will ofhighlight them to attract Most Most importantly, importantly, the system system will will make make use use of of the the quiz quiz results results of learners learners to to identify identify the the instructors’ attention. learners’ learners’acquired acquiredKPs KPsor orrelations relationsand andthen thensupport supportthe theinstructor instructorin indeciding decidinggroup groupdistribution distributionfor for Most importantly,activities the system will make useknowledge of the quiz results ofFor learners to as identify the collaborative collaborativelearning learning activitiesbased basedon onlearners’ learners’ knowledgestructures. structures. Forexample, example, asshown shownin in learners’ acquired KPs or relations and then support theto instructor incollaborative deciding group distribution for Figure 2, assume an instructor of a given course plans organize learning groups to Figure 2, assume an instructor of a given course plans to organize collaborative learning groups to collaborative learning activities based on learners’ knowledge structures. For example, as shownthe in study newKP KP which eight related KPs already in theTocourse. To encourage study aa new which hashas eight related KPs already taught taught in the course. encourage the cooperation Figure 2, assume an instructor a given course the plans to organize collaborative learning groups to cooperation between learners inofthe same group, system will identify Learners B and C, who between learners in the same group, the system will identify Learners A, B and C, whoA,acquired several study a new KP which has eight related KPs already taught in the course. To encourage the acquired several KPs (overlap is allowed) related to respectively, the target KP,and respectively, andthat recommend that KPs (overlap is allowed) related to the target KP, recommend the instructor cooperation between learners in the same group, the system will identifyin Learners A, Bsimply and C,know who the instructor put those three learners in one group. Otherwise, if learners one group put those three learners in one group. Otherwise, if learners in one group simply know the same acquired several KPs, KPs (overlap is allowed) related to the target KP, and recommend that the sameKPs, related it is difficult fortothem to realize theof rest therespectively, related knowledge reach related it is difficult for them realize the rest theofrelated knowledge and and reach the the full the instructor put those three learners in one group. Otherwise, if learners in one group simply know full understanding of the target effectively during learning process. Based on this principle, understanding of the target KP KP effectively during the the learning process. Based on this principle, the the same related KPs, it is difficult for themsupport to realizeinstructors the rest of the related knowledge and reach for the the system willdesigned be designed to visually in group deciding group distribution system will be to visually support instructors in deciding distribution for collaborative full understanding of activities. the target KP effectively during the learning process. Based on this principle, collaborative learning learning activities. the system will be designed to visually support instructors in deciding group distribution for collaborative learning activities.
Figure 2. Visualization support for instructors of e-book users. Figure 2. Visualization support for instructors of e-book users.
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Figure 2. Visualization support for instructors of e-book users.
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Systems 2016, 4, 21 3. A Course-Centered Ontology for the Visualization Learning Support System
3. A Course-Centered Ontology for the Visualization 3.1. Semi-Automatically Built Course-Centered Ontology Learning Support System
To facilitate the visualization supports mentioned 3.1. Semi-Automatically Built Course-Centered Ontology in the previous section, the description of the information about all the KPs and the relations between KPs is required for the system. In this paper, To facilitate the visualization supports mentioned in the previous section, the description of the we present a method develop a course-centered toIn describe all the information aboutto allsemi-automatically the KPs and the relations between KPs is required forontology the system. this paper, required fromtothe knowledge in courses. we information present a method semi-automatically develop a course-centered ontology to describe all the Firstly, weinformation automatically the information from the “Syllabus system” of Kyushu University required fromexact the knowledge in courses. and createFirstly, the basic of the course-centered ontology. shown insystem” Figure 3, example, weframework automatically exact the information from theAs“Syllabus of for Kyushu and createin thethe basic framework of theDepartment, course-centered ontology. As shown Figure 3, in for theUniversity Kikan Education Arts and Science there are 3730 coursesinregistered for example, for the Education in there the Arts Science Department, are 3730 courses the syllabus system. ForKikan one course title, areand several courses taughtthere by different professors registered in the syllabus system. For one course title, there are several courses taught by different or instructors; those courses may share some of the same keywords or have completely different professors or instructors; those courses may share some of the same keywords or have completely keywords. One keyword also can be shared by courses which have different course titles. For example, different keywords. One keyword also can be shared by courses which have different course titles. “Differential equation” is taught in both “KIKAN Physics I A” and “Calculus and exercises” courses. For example, “Differential equation” is taught in both “KIKAN Physics I A” and “Calculus and Another typical example “Entropy”, which is not only taught in Thermodynamics exercises” courses. is Another typical example is “Entropy”, which is not onlycourses taughtbut in also in Information theory courses. In this step, the information of “course title/numbering code”, “course Thermodynamics courses but also in Information theory courses. In this step, the information of code”“course and “keywords” fromcode”, the syllabus automatically exacted used forare theautomatically construction of title/numbering “course are code” and “keywords” fromand the syllabus exacted and usedfor forontology. the construction of the basic framework for ontology. the basic framework
Figure 3. The framework of of thethe course-centered basedononsyllabus syllabus information. Figure 3. basic The basic framework course-centered ontology ontology based information.
However, for most of the courses in the syllabus system, less than 10 keywords are described;
However, for most of the courses in the syllabus system, less than 10 keywords are described; the basic ontology framework built on those syllabus keywords is sufficient to provide the the basic ontology framework built on those syllabus keywords is sufficient to provide the visualization support described in the previous section. Therefore, in the second step, we encourage visualization support described in themodify previous in the second step, we[12] encourage professors/instructors to manually the section. ontology Therefore, using the ‘Protégé’ ontology editor and professors/instructors to manually modify the ontology using the ‘Protégé’ ontology editor [12] and then upload the modified ontology with the description of its related PDF file IDs in BookLooper. then upload modified ontology with theand description related design PDF file IDs in described BookLooper. Forthe building a demo, we applied adjusted of theits ontology method by For building applied and ontology adjustedofthe ontology designscience method described Wang et al. [7] atodemo, developwe a course-centered an existing computer course, called by ontology of computer science (COCS). Weexisting analyzedcomputer the learning materials of this Wang course-centered et al. [7] to develop a course-centered ontology of an science course, called computer science course, extracted about 100 KPs and about 20 kinds of relations, and defined them course-centered ontology of computer science (COCS). We analyzed the learning materials of this in COCS. Figure 4 illustrates some KPs100 andKPs theirand relations will describe way inthem computer science course, extracted about aboutin 20COCS. kinds We of relations, andthe defined which COCS is designed and developed to show professors/instructors of other courses how to in COCS. Figure 4 illustrates some KPs and their relations in COCS. We will describe the way in build up their course-centered ontology. Without doubt, how to combine all the course-centered which COCS is designed and developed to show professors/instructors of other courses how to build ontology made by different professors/instructors into one ontology will be another issue we need to up their course-centered discuss in the future. ontology. Without doubt, how to combine all the course-centered ontology made by different professors/instructors into one ontology will be another issue we need to discuss in 4 the future.
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Figure 4. The course-centered ontology of computer science (COCS). Figure 4. The course-centered ontology of computer science (COCS).
In the last step, we made a tool to automatically identify the location (including the file ID and In the last step, a tool automatically identify the location (including the file ID and the page number) ofwe themade KPs in the to BookLooper system and put those location information details the page number) of the KPs in the BookLooper system and put those location information details into into the ontology. the ontology. Based on these three steps, the course-centered ontology will be developed semi-automatically. Basediton steps, course-centered ontology willtime-consuming. be developed semi-automatically. However, is these worththree noting thatthe maintaining this ontology is still However, it is worth noting that maintaining this ontology is still time-consuming. 3.2. A Visualization Learning Support System Providing a Knowledge Comparison Environment 3.2. A Visualization Learning Support System Providing a Knowledge Comparison Environment To automatically manipulate the course-centered ontology which describes all the relations To automatically manipulate the course-centered ontology which describes all the relations between KPs, a system which is intended to provide visualization learning supports for the between KPs, a system which is intended to provide visualization learning supports for the construction construction of learner knowledge frameworks is developed. Figure 5 shows a learner’s view of the of learner knowledge frameworks is developed. Figure 5 shows a learner’s view of the computer computer science course in the VSSE. science course in the VSSE. In the left part of this view, all the concepts of COCS are shown by a tree structure. Users can In the left part of this view, all the concepts of COCS are shown by a tree structure. Users can open all the concepts level by level until reaching the KP they are seeking. Further, a search function open all the concepts level by level until reaching the KP they are seeking. Further, a search function is is also provided to the right, above the tree structure. The learner can set the time period (for also provided to the right, above the tree structure. The learner can set the time period (for example, example, from 3 January 2016 to 4 January 2016) and push the search button; then, the KPs which are from 3 January 2016 to 4 January 2016) and push the search button; then, the KPs which are involved involved in the pages that the learner reads during that time period will be highlighted. In addition, in the pages that the learner reads during that time period will be highlighted. In addition, the learner the learner can search items by keywords, and items which contain the keywords in the tree can search items by keywords, and items which contain the keywords in the tree structure will also be structure will also be highlighted to enable further checking. highlighted to enable further checking. When the user double-clicks one leaf which represents one KP, the right relation panel will When the user double-clicks one leaf which represents one KP, the right relation panel will display display this KP and all its related KPs lined by relations defined in COCS. For example, in Figure 5 this KP and all its related KPs lined by relations defined in COCS. For example, in Figure 5 the the individual which represents the KP “shift_JIS” is chosen. Then users can get a visual individual which represents the KP “shift_JIS” is chosen. Then users can get a visual representation of representation of relevant information as shown in the relation panel. relevant information as shown in the relation panel. Also, when the user moves the mouse on every node shown in the relation panel, the essential Also, when the user moves the mouse on every node shown in the relation panel, the essential properties of that KP (represented by data properties of one individual in COCS) will be listed, while properties of that KP (represented by data properties of one individual in COCS) will be listed, while for every arc shown in the relations panel, the relation statement will be displayed (for example, the for every arc shown in the relations panel, the relation statement will be displayed (for example, the displayed relation axiom between “shift_JIS” and “JIS_X_0201” in Figure 5). Therefore, users can get displayed relation axiom between “shift_JIS” and “JIS_X_0201” in Figure 5). Therefore, users can the essential properties of every KP and all its related KPs from the relations panel conveniently. All get the essential properties of every KP and all its related KPs from the relations panel conveniently. this information is extracted automatically from the OWL (Web Ontology Language for Semantic All this information is extracted automatically from the OWL (Web Ontology Language for Semantic Web, designed by W3C) [15] file of COCS. Furthermore, if there are too many relations shown in the Web, designed by W3C) [13] file of COCS. Furthermore, if there are too many relations shown in the relation panel, the user can select the relations of interest with the Arc Types panel. relation panel, the user can select the relations of interest with the Arc Types panel.
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Figure 5. The main interface of VSSE for learners. Figure 5. The main interface of VSSE for learners.
This system’s function is expected to provide the visualization support for the construction of This system’s function is expected provide the visualization support theinstructors construction learner knowledge frameworks. The to other function, which is intended tofor help to of learner knowledge frameworks. The other function, intended todistribution help instructors to understand understand the learners’ knowledge structures andwhich easily is decide group for collaborative thelearning learners’ knowledge and easily decide group distribution for collaborative learning activities, is stillstructures under development. activities, is still under development. 4. Conclusions and Further Work 4. Conclusions and Further Work In summary, we present a semi-automated method to construct a course-centered ontology, and based on we thispresent Course-centered Ontology we design and develop a system ontology, to provide In summary, a semi-automated method to construct a course-centered and visualization support for BookLooper e-book users. For learners of BookLooper users, the system is based on this Course-centered Ontology we design and develop a system to provide visualization designed the perspective of meaningful learning to visually users, supportthe them to effectively support for from BookLooper e-book users. For learners of BookLooper system is designed construct their knowledge framework. For instructors of BookLooper users, when they construct are planning from the perspective of meaningful learning to visually support them to effectively their collaborative learning activities, the system can provide visualization support to help them decide knowledge framework. For instructors of BookLooper users, when they are planning collaborative the group distribution based oncan knowledge of learners. learning activities, the system providestructure visualization support to help them decide the group After the development of these two mentioned functions, the visualization learning support distribution based on knowledge structure of learners. system based on the ontology technique will be evaluated from various perspectives. In our further After the development of these two mentioned functions, the visualization learning support work, to encourage active engagement, a timely guided discovery learning environment [13] called system based on the ontology technique will be evaluated from various perspectives. In our further “cache-cache comparison” [14] will be developed to encourage the learner to detect hidden relations work, to encourage active engagement, a timely guided discovery learning environment [14] called between relevant KPs or hidden acquired KPs from given relevant KPs and relations through “cache-cache comparison” [15] will be developed to encourage the learner to detect hidden relations reflecting on the attributes of acquired knowledge visually. If learners keep making mistakes during between relevant KPs or hidden acquired KPs from given relevant KPs and relations through reflecting the task the system will provide hints to help them rethink and get closer to the correct answer. The onprocess the attributes of acquired learners keep mistakes during the task of seeking hidden knowledge relations orvisually. conceptsIf is intended to making encourage learners to locate newthe system will provide hints to help them rethink and get closer to the correct answer. The process knowledge in their knowledge structure and restructure existing knowledge; meanwhile, the of seeking hidden relations or concepts and is intended to encourage learners to locate new knowledge iterative procedure of confirmation modification in their own relation map ensures that they in their knowledge structure and restructure existing knowledge; meanwhile, the iterative procedure of check the logical consistency of their ideas and clear up misunderstandings. confirmation and modification in their own relation map ensures that they check the logical consistency Acknowledgments: The research is supported by Kyushu University Interdisciplinary Programs in Education of their ideas and clear up misunderstandings.
and Projects in Research Development, the Research and Development on Fundamental and Utilization Technologies for Social Big Data (No. 178A03), and the Commissioned Research Programs of National Institute ofand Acknowledgments: The research is supported by Kyushu University Interdisciplinary in Education Information and Communications Technology, Japan. Projects in Research Development, the Research and Development on Fundamental and Utilization Technologies for Social Big Data (No. 178A03), and the Commissioned Research of National Institute of Information and Author Contributions: Jingyun Wang and Hiroaki Ogata conceived and designed the system function; Jingyun Communications Technology, Japan. Wang and Atsushi Shimada worked on the ontology design, and Atsushi Shimada provided the maintaining Author Contributions: Jingyun Wang and Hiroaki Ogata conceived and designed the system function; information for the ontology; Jingyun Wang performed the development of the ontology and the system and Jingyun Wang and Atsushi Shimada worked on the ontology design, and Atsushi Shimada provided the wrote the paper. maintaining information for the ontology; Jingyun Wang performed the development of the ontology and
the system and wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest.
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Abbreviations The following abbreviations are used in this manuscript: KP VSSE
Knowledge Point visualization support system for E-book users
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