The multi-user computer-aided design collaborative learning framework

New developments to computer-aided design (CAD) software transform a once solitary modelling task into a collaborative one. The emerging multi-user CAD (MUCAD) systems allow virtual, real-time collaboration, with the potential to expand the learning outcomes and teaching methods of CAD. This paper proposes a MUCAD collaborative learning framework (MUCAD-CLF) to interpret backend analytic data from commercially available MUCAD software. The framework builds on several existing metrics from the literature and introduces newly developed methods to classify CAD actions collected from users’ analytic data. The framework contains two different classification approaches of user actions, categorizing actions by action type (e.g., creating, revising, viewing) and by design space (e.g., constructive, organizing), for comparative analysis. Next, the analytical framework is applied via a collaborative design challenge, corresponding to over 20,000 actions collected from 31 participants. Illustrative analyses utilizing the MUCAD-CLF are presented to demonstrate the resulting insight. Differences in CAD behaviour, indicating differences in learning, are observed between teams made up entirely of novices, entirely of experienced users, or a mix. In pairs of experts and novices, we see both a perceived high-satisfaction apprenticeship experience for the novices and preliminary evidence of an increase in expert design behaviours for the novices. The proposed framework is critical for MUCAD systems to make the most of the educational possibility of combining technical skill-building with team collaboration. Preliminary evidence collected in a fully-virtual design learning activity, and analyzed using the proposed MUCAD-CLF, shows that novice students gain advanced CAD design knowledge when collaborating with experienced teammates. With the user data captured by modern MUCAD software and the MUCAD-CLF presented herein, instructors and researchers can more efficiently assess and visualize students’ performance over the design learning process.     

Design of adaptive hypermedia learning systems: A cognitive style approach

In the past decade, a number of adaptive hypermedia learning systems have been developed. However, most of these systems tailor presentation content and navigational support solely according to students’ prior knowledge. On the other hand, previous research suggested that cognitive styles significantly affect student learning because they refer to how learners process and organize information. To this end, the study presented in this paper developed an adaptive hypermedia learning system tailored to students’ cognitive styles, with an emphasis on Pask’s Holist–Serialist dimension. How students react to this adaptive hypermedia learning system, including both learning performance and perceptions, was examined in this study. Forty-four undergraduate and postgraduate students participated in the study. The findings indicated that, in general, adapting to cognitive styles improves student learning. The results also showed that the adaptive hypermedia learning system have more effects on students’ perceptions than performance. The implications of these results for the design of adaptive hypermedia learning systems are discussed.     

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

Research on self-regulated learning (SRL) in hypermedia-learning environments is a growing area of interest, and prior knowledge can influence how students interact with these systems. One hundred twelve (N = 112) undergraduate students’ interactions with MetaTutor, a multi-agent, hypermedia-based learning environment, were investigated, including how prior knowledge affected their use of SRL strategies. We expected that students with high prior knowledge would engage in significantly more cognitive and metacognitive SRL strategies, engage in different sequences of SRL strategies, spend more time engaging in SRL processes, and visit more pages that were relevant to their sub-goals than students with low prior knowledge. Results showed significant differences in the total use of SRL strategies between prior knowledge groups, and more specifically, revealed significant differences in the use of each metacognitive strategy (e.g., judgment of learning), but not each cognitive strategy (e.g., taking notes) between prior knowledge groups. Results also revealed different sequences of use of SRL strategies between prior knowledge groups, and that students spent different amounts of time engaging in SRL processes; however, all students visited similar numbers of relevant pages. These results have important implications on designing multi-agent, hypermedia environments; we can design pedagogical agents that adapt to students’ learning needs, based on their prior knowledge levels.     

Hypermedia learning and prior knowledge: domain expertise vs. system expertise

Abstract Prior knowledge is often argued to be an important determinant in hypermedia learning, and may be thought of as including two important elements: domain expertise and system expertise. However, there has been a lack of research considering these issues together. In an attempt to address this shortcoming, this paper presents a study that examines how domain expertise and system expertise influence students' learning performance in, and perceptions of, a hypermedia system. The results indicate that participants with lower domain knowledge show a greater improvement in their learning performance than those with higher domain knowledge. Furthermore, those who enjoy using the Web more are likely to have positive perceptions of non-linear interaction. Discussions on how to accommodate the different needs of students with varying levels of prior knowledge are provided based on the results.     

Information problem solving by experts and novices: analysis of a complex cognitive skill

In (higher) education students are often faced with information problems: tasks or assignments that require them to identify information needs, locate corresponding information sources, extract and organize relevant information from each source, and synthesize information from a variety of sources. It is often assumed that students master this complex cognitive skill of information problem solving all by themselves. In our point of view, however, explicit and intensive instruction is necessary. A skill decomposition is needed in order to design instruction that fosters the development of information problem solving. This research analyzes the information problem solving process of novices and experts in order to reach a detailed skill decomposition. Results reveal that experts spend more time on the main skill ‘define problem’ and more often activate their prior knowledge, elaborate on the content, and regulate their process. Furthermore, experts and novices show little differences in the way they search the Internet. These findings formed the basis for formulating instructional guidelines.     

Cognitive learning efficiency through the use of design patterns in teaching

Nowadays we need to teach students how to become flexible problem solvers in a dynamic world. The pace in which technology changes and complexity increases requires increased efficiency in learning and understanding. This requires the engineers of tomorrow to quickly gain knowledge and insight outside their prime area of expertise. To transfer practical, how-to knowledge, to re-use design solutions and to teach students design solutions in the context of a specific domain, design patterns can be used. Design patterns offer a way to transfer knowledge that is more practical and ‘ready for use’ than a generic theory-based transfer of knowledge. However, the advantage of design patterns might go beyond re-use, design efficiency and flexibility. This paper argues that in addition to the benefits described above, there is a specific added value for the use of design patterns by students to acquire design skills and domain knowledge. To analyze this proposition we will analyze the literature on cognitive load and cognitive learning processes, and relate this to experiences from three case studies in which novices and experts were offered design patterns to develop and implement systems and processes. We will reflect on implications of the use of design patterns in computer-based teaching as well as on a potential support tool to improve the accessibility of pattern languages.     

Learning with hypermedia: The influence of representational formats and different levels of learner control on performance and learning behavior

In this paper, two experiments on the use of hypermedia environments for learning about probability theory are reported. In Experiment 1a it was tested whether multimedia design principles (multimedia principle, modality principle, redundancy principle) are valid in hypermedia environments, despite the fact that hypermedia offers more learner control than multimedia. The results showed only little evidence for this validity, although the hypermedia environment entailed only a rather low level of learner control. In Experiment 1b it was investigated how learner control affects performance and how its possible impact is moderated by learners’ prior knowledge. A high level of learner control positively affected the effectiveness of instruction only with regard to intuitive knowledge, but was at the same time accompanied by large increases in learning time, thereby rendering the instruction inefficient. Unexpectedly, effects of learner control were not moderated by students’ prior knowledge. The results imply that the idea to use multimedia design principles for hypermedia learning is too simple and that the benefits and drawbacks of learner control depend heavily on learning objectives and time constraints.     

Implement web learning environment based on data mining

The need for providing learners with web-based learning content that match their accessibility needs and preferences, as well as providing ways to match learning content to user’s devices has been identified as an important issue in accessible educational environment. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. In order to achieve optimal efficiency in a learning process, individual learner’s cognitive learning style should be taken into account. Due to different types of learners using these systems, it is necessary to provide them with an individualized learning support system. However, the design and development of web-based learning environments for people with special abilities has been addressed so far by the development of hypermedia and multimedia based on educational content. In this paper a framework of individual web-based learning system is presented by focusing on learner’s cognitive learning process, learning pattern and activities, as well as the technology support needed. Based on the learner-centered mode and cognitive learning theory, we demonstrate an online course design and development that supports the students with the learning flexibility and the adaptability. The proposed framework utilizes data mining algorithm for representing and extracting a dynamic learning process and learning pattern to support students’ deep learning, efficient tutoring and collaboration in web-based learning environment. And experiments do prove that it is feasible to use the method to develop an individual web-based learning system, which is valuable for further study in more depth.     

DISCOVER: AN INTELLIGENT DISCOVERY PROGRAMMING SYSTEM

Much of research in programming environments for novices has been polarized toward two opposite domains: guided programming environments and free programming environments. Intelligent programming tutors and systems that represent guided environments concentrate mainly on helping novices in acquiring programming skills through a series of problem-solving situations. Most of these systems tend to ignore the significance of a preproblem solving, dynamic, visualization-based, and free environment which would help novices in compiling an effective programming knowledge through a discovery-like process. Free programming environments, on the other hand, concentrate exclusively on helping novices to build correct programming knowledge through visible, exploratory, and free discovery environments. These systems generally tend to neglect the issue of supporting those aspects of intelligent diagnosis and tutoring, through which novices can transform their program knowledge into programming skill. A system has been developed called DISCOVER that occupies a unique niche between the two domains discussed above. The system emphasizes a synthesis-based framework which integrates free programming with guided programming features, and supports software visualization and immediacy of feedback to come up with an environment that facilitates learning programming through discovery and guidance. To assess the usefulness of the design framework presented in this paper, two pilot performance evaluations were conducted. The results provided a number of interesting insights into the implication of incorporating visualization and immediacy features, along with intelligent program diagnosis into the design of programming systems intended mainly for novices.     

The MDS-ANAVA technique for assessing knowledge representationdifferences between skill groups

Knowledge representation is one of important factors that determine human performance on cognitive tasks. Due to different levels of experience, different groups of people may develop different knowledge representations which lead to different levels of performance on cognitive tasks. If knowledge representation differences exist between skill groups such as experts and novices, those differences can be used to guide the training of novices for skill acquisition, and to assist the design of jobs and tools for performance enhancement. A technique is presented in this paper for assessing knowledge representation differences between skill groups, based on multidimensional scaling (MDS) of dissimilarity data and analysis of angular variance (ANAVA). The MDS-ANAVA technique was applied to two sets of dissimilarity data that were obtained from ten experts and ten novices in the computer domain, one set concerning 23 concepts in C computer programming, and another set concerning 21 concepts in the UNIX operating system. Knowledge representation differences from the MDS-ANAVA technique are compared with those from the hierarchical clustering technique. The MDS-ANAVA technique shows several advantages to the hierarchical clustering technique in testing the statistical significance of knowledge representation differences between skill groups and revealing features underlying knowledge representations of skill groups     

Students’ navigation patterns in the interaction with a mechanics hypermedia program

This study investigates the interaction of a group of freshmen enrolled in a Pre Service Physics Teacher Training Course with a mechanics hypermedia program. Data were obtained to discuss hypertextual navigation guided by the following questions: (i) How can the students’ navigation in this hypermedia program be characterized? (ii) How does this relate to their prior knowledge in mechanics? The sequence analysis of the events collected from the log files was used to characterize students’ navigation and a mechanics test assessed students’ prior knowledge. The inspection of students’ navigation graphs made it possible to associate the structure of navigation to prior knowledge in mechanics. Three patterns of navigation are proposed associated to different levels of students’ prior knowledge and to different roles performed by the program. In the organized navigation, the student who best performed in the pre test seemed to be reviewing content he already knew, using the system as a database. In the conceptual navigation the students who presented difficulties in the pre test spent different times in the pages as they were addressing conceptual difficulties, using the system as a support for learning. The students who scored the lowest in the test performed a disoriented navigation, spending much less than the adequate time to interact meaningfully with the content. The role that previous knowledge in mechanics plays in these patterns of navigation was related to the function that Ausubel’s subsumers perform in learning. The results indicate that hypertextual navigation can provide information about students’ conditions to engage in meaningful learning, which could be used to help the teacher personalize instruction.     

Expert-novice knowledge of computer programming at different levels of abstraction

Computer programming knowledge can be classified into five levels of abstraction: objective, conceptual, functional, logical, and physical. An experiment was carried out to determine whether the mastering of knowledge at different levels of abstraction changed with the level of skill. Ten experts and ten novices in C computer programming participated in the experiment. The subjects' knowledge at the five levels of abstraction was tested through 20 multiple-choice questions. The experimental results indicated that knowledge differences between experts and novices at an abstract level or a concrete level depended on what abstract or concrete knowledge was implied. Experts had better abstract knowledge than novices at the conceptual and functional levels but not at the objective level. Experts had better concrete knowledge than novices at the physical level but not at the logical level. The classification of computer programming knowledge in levels of abstraction and the experimental results helped in clarifying a general finding from previous studies that experts had better abstract knowledge than novices.     

The relationship between the learning strategies and learning styles in a hypermedia environment

The relationship between hypermedia-assisted instruction and learners' different learning styles has prompted tremendous interest in educators because accommodating learners' different needs is one of the promises hypermedia technology is believed to hold. This study examined the different learning strategies by Field-independent (FI) people and Fielddependent (FD) people in a hypermedia assisted language learning setting. Sixty-three college, international students participated in the study. Findings that describe the different types of media, tools, and learning aids chosen by the FI and FD students were reported. Results from the study suggested that different learning style groups employed different learning strategies in accomplishing the same task. The results of the study indicated that the hypermedia technology has the potential to accommodate learners with different needs through its rich environment.     

Semantic scaffolds in hypermedia learning environments

In knowledge acquisition from hypermedia, learners have to orient themselves in a non-linear information space, navigate in this space and build a knowledge structure from the information there. Research on learning from hypermedia has focused primarily on enhancing orientation and navigation, with only minor attention on possibilities for supporting semantic processing. In a first experiment, 98 students from senior high school and university undergraduates learned about a complex subject matter either without or with semantic scaffolds, which were presented either in an obligatory (non-removable) or in an optional mode. High prior knowledge learners profited from adequately used semantic scaffolds, whereas low prior knowledge learners did not. In a second experiment, 53 senior high school students and university undergraduates received the learning material with semantic scaffolds presented either in an obligatory mode (but removable on demand) or in an optional mode. Learners with low prior knowledge performed better with optional presentation. Learners with high prior knowledge performed better with obligatory presentation, which allowed removing scaffolds on demand. Learners generally preferred the optional presentation of learning scaffolds.     

Adaptive Hypermedia

Adaptive hypermedia is a relatively new direction of research on the crossroads of hypermedia and user modeling. Adaptive hypermedia systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user. The goal of this paper is to present the state of the art in adaptive hypermedia at the eve of the year 2000, and to highlight some prospects for the future. This paper attempts to serve both the newcomers and the experts in the area of adaptive hypermedia by building on an earlier comprehensive review (Brusilovsky, 1996; Brusilovsky, 1998).     

A knowledge extraction and representation system for narrative analysis in the construction industry

Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students’ learning.     

Integrated knowledge acquisition architectures

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.     

Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge

Students learn new instructions well by building on relevant prior knowledge, as it affects how instructors and students interact with the learning materials. Moreover, studies have found that good prior knowledge can enable students to attain better learning motivation, comprehension, and performance. This suggests it is important to assist students in obtaining the relevant prior knowledge, as this can enable them to engage meaningfully with the learning materials. Tests are often used to help instructors assess students’ prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to each student, and this may mean that students are unable to realize their own individual weaknesses. To address this problem, instructors can diagnose the test results to provide more detailed information to each student, but this is obviously a time-consuming process. Therefore, this study proposes a testing-based diagnosis system to assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken. Furthermore, an experiment was conducted to evaluate the effectiveness of the proposed approach in an interdisciplinary course, since several studies have indicated that students learn more and better in such courses when applying relevant prior knowledge to what they are learning. The experimental results show that the developed system is able to effectively diagnose students’ prior knowledge and enhance their learning motivation and performance on an interdisciplinary course. In addition, two diagnostic evaluations were also conducted to assess whether the diagnoses given by the system were consistent with the decisions of experts. The results demonstrate that the proposed system can effectively assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken, since the diagnoses produced by the system were broadly consistent with those of experts.     

Hypermedia systems for improving knowledge,understanding and skills in engineering degree courses

Two contrasting computer based learning applications have been introduced into engineering degree courses. Both are based upon the “open” hypermedia approach, and both use Microcosm to organise and link resources, and Toolbook to present much of the material. One of the applications uses a traditional, “linear”, tutorial style approach, while the other presents students with a design problem and provides them with a large and diverse amount of poorly structured information which the students have to use to generate an engineering design. Both applications were evaluated in several ways and analysis of the responses leads to the conclusion that carefully written computer based applications can be effective not only for teaching basic knowledge at this level, but can also be used as a means of allowing students to develop skills in complex areas such as generating solutions to open-ended mechanical engineering design problems.     

Proficiency-Adapted Information Browsing and Filtering in Hypermedia Educational Systems

We present a framework for proficiency-adapted information browsing and filtering in educational hypermedia systems. In hyperdocuments, information is acquired by browsing through highly interconnected sets of information nodes. In order to find specific information, users follow links to nodes they judge to be relevant. In order to help users find relevant information and new learning material that match their levels of domain knowledge, we present a framework for adapting the information nodes, and the links leading to them, to the user's proficiency in the subject matter. Such a proficiency-adapted, user-centered educational environment is intended to enhance learning. We believe that learning in educational hypertext-based applications cannot be reduced to traversing a static information space. Navigating through any space, be it a physical or an information space, normally requires that users have a prior degree of proficiency in the domain knowledge. Learning is an evolving dynamic process through which users progress from a situation of unfamiliarity to one of mastery of a knowledge corpus. Therefore, we propose a model of proficiency-adapted learning and information browsing in which the presented choices (links and the textual context of links) are selected based on the user's knowledge state. Ultimately, such an adaptive course not only guides the learning process of the student, but it gradually transforms itself into a reference guide. This revised version was published online in July 2006 with corrections to the Cover Date.