Advances in Web-Based Learning - ICWL 2011

We analyze the logs of an online mathematics game tournament, played simultaneously by thousands of students. Nearly 10,000 students, coming from 356 schools from all regions in Chile, registered to the fourth tournament instance. The children play in teams of 12 students from the same class, and send their personal bets to a central server every 2 minutes. Each competition lasts about one clock hour and takes place within school hours. Students are pre-registered and trained by their school teacher. The teacher is responsible for reviewing curriculum contents useful for improving performance at the game and coaches students participating in trial tournaments taking place a few weeks before the national tournament. All bets are recorded in a database that enables us to analyze later the sequence of bets made by each student. Using cluster analysis with this information, we have identified three types of players, each with a well-defined strategy.

In this paper we address common issues faced by students in programming courses by combining implicit and explicit feedback measures to provide real-time assistance in coding tasks. We also introduce our concept visualisation technique, which aims to visually convey programming concepts and information on the execution state to students. The mapping between game content construction actions and actual source code forms an implicit example-based learning environment, allowing programming concepts to be more clearly conveyed than in conventional integrated development environment (IDE) or static lecture materials. An experimental evaluation of a prototype system suggests the potential of this approach for programming education by scoring highly in terms of both user satisfaction and potential pedagogical capability.

This paper presents an extensible multi-platform educational game framework. This new framework enhances the

point-and-click

adventure game model used in a preexisting educational game framework (eAdventure) by extending it with new game metaphors and interactions. These include mini-games (e.g. puzzles, stories, word-games) suited for a greater variety of subjects (e.g. math, history, science) that are configured through a plug-in architecture. Targeting multiple platforms allows for the transparent deployment and use of the developed games in new mobile devices (e.g. tablets) and other systems of growing interest in the educational community. Unifying these aspects into one platform is a challenging task because it must implement the appropriate balance between expressivity, game production costs and possibilities to re-use successful educational game models. The basic adventure metaphor is used as a backbone to provide a strong narrative to drive games and engage the students.

Working with peers at computers may cultivate social interaction but what patterns of student-student talk are associated with statistical thinking in an IT environment are not well studied. An observation study focusing on social interaction was therefore conducted in the context of statistics classroom teaching. The observation study drew on Mercer’s work [9] to categorise student-student talk as exploratory, cumulative, or disputational. Most talk was of the exploratory type, characterised by reasoning and statistical thinking, and only a few instances were classified as cumulative when students were attempting straightforward learning tasks or maintaining harmonious social relations. No instances of disputational talk were observed. A finer grained analysis of students’ talk, using an adaptation of Kumpulainens’s framework [1], identified some forms of talk that were used for maintaining social interaction, while other forms were associated with making reasoning explicit and activating higher order thinking for a variety of sophisticated tasks, for instance, deducing practical implications for regression parameters.

Traditional teaching methods should be adapted to accommodate the learning needs of the new generation of digital native students. One approach is to provide support for social learning by integrating Web 2.0 tools in educational settings. This paper focuses on a platform specifically built to this end: eMUSE (

e

mpowering

M

ash

U

ps for

S

ocial

E

-learning), which aggregates several social media components by means of mashups. In addition, the platform provides learner tracking functionality, by retrieving students’ actions with each tool and storing them in a local database for further processing. Thus eMUSE offers value-added services for both students and teachers: common access point to facilitate tools’ management; help students keep track of their contributions as well as their peers’; a simple way for instructors to monitor the class activity as well as quickly check, visualize and grade each student’s contributions. The paper includes a comprehensive rationale underlying eMUSE, a description of the platform architecture and functionalities, as well as an initial experimental validation.

Micro-learning

refers to short-term learning activities on small learning units. In our contemporary mobile/web society, micro-learning pertains to small pieces of knowledge based on web resources. Micro-learning falls into the group of informal learning processes. The existing web and mobile services have great potential to support informal learning processes, especially micro-learning. However, several specific aspects need to be considered. In this paper, we propose a micro-learning model based on three technical aspects: (i) ubiquitous learning resource acquisition; (ii) cloud-based data management; and (iii) tag-based regulation of learning processes and content. A micro-learning prototype consisting of an Android application and a web browser add-on is evaluated in the use case of bilingual vocabulary learning. The initial prototype evaluation study shows promising results in enhanced flexibility in personal learning content creation and increased efficiency in filling knowledge gaps.

Suzuki believed that learning environment would determine the success and failure of students in music. Therefore, he not only provided teachers with useful and logical teaching skills, but also stressed on the importance of parents’ participation as a “home teacher”. In this paper, we propose a Web 2.0 learning platform (WASSAP) to support Suzuki method of music education, especially aiming at supporting busy parents who do not have adequate time to accompany their child in person to learn. Alerts are used as a mechanism to facilitate communications among parents, teachers, and peers.

This paper focuses on relations between learners’ generic level of trust in online environments and their attitudes towards openness and sharing in a blog-based personal learning environment LePress. The main rationale of this study was inspired by the changes present in today’s education, where the use of blogs as Personal Learning Environments is becoming an emerging trend in higher education. We claim that by understanding interrelation of trust and attitudes towards sharing within this open and flexible environments, we will be able to contribute to the design decisions regarding the LePress, which lead to increasing the participation of individuals and communities in inherent formal and informal learning processes in a number of contexts. Major contributions of this paper are towards understanding (1) the relationship between trust and teacher/learners attitudes towards sharing in a blog-based personal learning environment and (2) the correlation between users trust level and expectations towards affordances of online learning tools.

In recent years, the development of E-learning is rapid. Learning efficiency can be greatly improved if E-learning users’ social networks properties can be effectively utilized. However, the nodes in most research models are the same type. The focus of our study is on E-learners’ positive influence between their relationship. In this paper, we proposed a new model and selection algorithm named Weight Positive Influence Dominating Set (WPIDS) and analyzed its efficiency through a case study. By comparing the differences between WPIDS and that of Positive Influence Dominating Set (PIDS), we found that our model and algorithm are more effective than those of PIDS.

In this paper, we investigate how social tagging could be used in Education as a support for learning processes. We first summarize the results of recent works on the effect of the use of social tagging for knowledge building and learning. We show that few educational tag-based systems intentionally use tagging to enhance learning. We then describe a Tag-based Collaborative System (TaCS), meant for supporting social and collaborative learning thanks to tagging, and detail the learning processes expected by the use of the system. We conducted an exploratory study to observe (1) the evolution of the students’ tags as an indicator of the learning of new concepts and (2) the evolution of the tags assigned to documents as an indicator of the learning of new conceptual relations. The results show that the students make their tags and their relations to documents evolve, mainly due to two activities: the comparison of individual and collective tag clouds and the negotiation for an agreement on a common tag cloud in the groups.

This paper introduces a framework for collaborative microblogging that we believe is useful in enriching student interaction, both in-class and outside of contact hours. The framework is called Microblogging for Community of Inquiry (MiCoI). MiCoI is based on our experience using microblogging in an undergraduate Information Systems unit to facilitate a Community of Inquiry (CoI). In our experiment we demonstrated the existence of CoI with collaboration between an Australian and American tertiary institution using Twitter as the microblogging platform. The results were encouraging and were highlighted by the opportunity of cross-institutional interaction, reduced barrier to student engagement driven by social elements and additional channels to facilitate CoI. MiCoI is extended from this experiment and is designed to be applicable to any microblogging platform.

In this paper we first investigate how eTwinning and national and local teachers’ professional development schemes interact. eTwinning is the community for schools in Europe that promotes teacher and school collaboration through the use of Information and Communication Technologies (ICT) under the European Union’s Lifelong Learning Programme. The eTwinning Portal hosts more than 137,000 teachers who interact with each other on the European-scale. Second, using this authentic data, we discuss how novel research methods such as Social Network Analysis, information visualisation techniques and future scenario forecasting are used to study eTwinning in the Tellnet-project aiming to sustain and support dynamic teacher networks as a platform for formal and informal teachers’ professional development in the future.

Although there are several competency frameworks for measuring teachers’ educational technology competencies, there is a lack of Web-based assessment tools that allow authentic assessment. This paper addresses the design challenges for assessing teacher’s educational technology competencies. The empirical part describes the participatory design process for developing a Web-based self- and peer-assessment tool. The presented conceptual design describes a system where teachers can solve competency tests that contain various self-test, peer-assessment and self-reflection tasks. The tasks are assessed using the performance indicators and assessment rubric that are based on the ISTE NETS for Teachers competency model.

For supporting technology-enhanced in-service teacher accreditation for professional competences, this case study in Estonian context investigates the applicability of an organizational knowledge-management (KM) model extended by the principles of self-regulated learning. The survey findings revealed the existing state, open challenges, and potential barriers of supporting teachers’ lifelong learning activities with the new model. An activity scenario and a set of software services supporting the harmonization of teachers’ accreditation with the model were developed via a participatory design process with the teachers. The evaluation indicated that organizational, intra- and inter-personal scaffolds embedded in services were used concurrently in each accreditation activity.

Many students do not manage to complete their higher education studies because they chose an university department whose curriculum consists of courses that are out of their interest. If the selection of their studies is done with more caution and knowledge might lead to better decision making. This paper presents a web-based decision support system which allows the student to indicate the degree of interest for courses in secondary education that they has been taught. The system, using the multicreteria analysis method ELECTRE, presents a possible, ranked based on student’s interests, university departments list.

This paper introduces the concept of a currency-free competence exchange platform in the context of communities of self-regulated learners exploiting personal learning environments. In a life-long perspective, learners can be seen as potential teachers of the skills they collect and develop all along their experience. Currently, spontaneous assistance is a common practice but remains limited to acquaintance groups. Our goal is to foster the emergence of self-driven communities by enabling learners to make available or find competences, to arrange coaching sessions, and to get reusable credits in return. This paper details our investigations on competence models and decentralized credit systems on which the design of our Competence Bartering Platform is grounded.

Semantic Web provides huge potential and opportunities for developing the next generation of e-learning systems. Although ontologies have a set of basic implicit reasoning mechanisms derived from the description logic, they need rules to make further inferences and to express relations that cannot be represented by ontological reasoning. We implemented an adaptive and intelligent web-based PRogramming TUtoring System – Protus. One of the most important features of Protus is the adaptation of the presentation and navigation of a course material based on particular learner knowledge. This system aims at automatically guiding the learner’s activities and recommend relevant actions during the learning process. This paper describes the functionality, structure and implementation of a learner model used in Protus as well as syntax of SWRL rules implemented for on-the-fly update of learner model ontology.

In recent years, computer-aided test-sheet composition has become an effective method to evaluate students’ learning level. To meet the needs of personalized test-sheet assembling for every student, a new test-sheet construction model is proposed. Based on the model, a personalized genetic algorithm (PGA) is proposed to assemble appropriate test sheets for individual students according to their different levels in mastered concepts of subjects. The proposed approach incorporates personalized information as preference gene bit into the crossover operator of genetic algorithm and fitness function, so as to select more non-mastered questions in the final test. In the experiments, the proposed algorithm was applied to assemble a series of items for students and the results demonstrate that the proposed approach is capable of effectively assembling personalized test sheets that meet the needs of different students and achieve good performance.

Student progress is critical for determining proper learning materials and their dissemination schedules in an e-learning system. However, existing work usually identifies student progress by scoring subject specific attributes or by determining status about task completion, which are too simple to suggest how teaching and learning strategies can be adjusted for improving student performance. To address this, we propose a set of student progress indicators based on the fuzzy cognitive map to comprehensively describe student progress on various aspects together with their causal relationships. These indicators are built on top of a student attribute matrix that models both performance and non-performance based student attributes, and a progress potential function that evaluates student achievement and development of such attributes. We have illustrated our method by using real academic performance data collected from 60 high school students. Experimental results show that our work can offer both teachers and students a better understanding on student progress.

In the Technology Enhanced Learning (TEL) domain, visualizations are attracting increased interest. In this paper, we present the Student Activity Meter that visualizes learner activities within online learning environments for learners and teachers to help increase awareness and to support self-reflection. We present evaluation results of two case studies with teachers, learning analytics students and experts. Results from teachers show that the visualizations can assist in creating awareness, understanding of student resource use and student time spending behavior. SAM’s three visualizations were perceived as equally useful, but for different tasks. The evaluation participants also identified new metrics to extend our current set and prioritized new visualization ideas.

Our research project identifies antecedents of students’ intention to voluntarily watch online theory videos available through an online learning platform. This paper extends the classic UTAUT by introducing the student year in the model next to three constructs that were derived from literature on student satisfaction with classic (offline) classes: perceived content usefulness, perceived e-learning enjoyment and facilitating conditions. Statistical tests (on a dataset of 766 students in three different years) show these four new constructs are statistically significant antecedents of the students’ intent to use the new feature. The

perceived content usefulness

and

perceived e-learning enjoyment

seem as important as the main UTAUT constructs

performance expectancy

and

effort expectancy

. The R² of our model is considerably higher than that of the UTAUT when applied to our dataset. We also detect moderating effects.

Today e-learning is embraced for delivering education and training as it offers various potential benefits. However, successful uptake of e-learning depends on a cluster of personal, technological, institutional and domain-specific (content) factors. Based on our literature review and empirical results of our previous work, we have identified specific attributes subsumed by each of these factors and integrated them into our model of student e-learning readiness. To validate the model, we have conducted a web-based survey to investigate the extent to which students in the Higher Education Institutions (HEIs) in Turkey offering the subject of electricity are ready for e-learning. 704 responses from the students of 417 departments in the related HEIs have been collected; 425 were complete responses. Whilst the findings revealed that the students were sufficiently ready for e-learning, training for e-learning is considered essential for enhancing student e-learning readiness.

Recently, social media enhanced learning has become more and more popular. It is featured as learning through interaction and collaboration in a community or across a social network, which can be considered as a kind of social learning. In this study, we integrate SNS (such as twitter) into the web-based learning process and further delve into the discovery of potential information from the reorganized stream data. We propose a Dynamical Socialized User Networking (DSUN) model which represents users’ profiling and dynamical relationship by a set of measures. Finally, we show an application scenario of the DSUN model to assist the learning process and enhance the learning efficiency in web-based environments.

To provide adaptive learning, an e-learning system needs to gather information about what student state is while the student learns online course. A student state index is the length of time the student stays on a webpage of online course. By predicting student’s stay time length, the e-learning system has potential to dynamically tailor the learning content to the students. A literature review is conducted on power law of learning and knowledge component. We assume that online course consists of knowledge components. A knowledge component crosses several successive web pages. Accordingly, an initial prediction method is proposed based on the two learning curve modes and the grey models. Based on the experimental result of this initial prediction method, construction method of grey models is modified. The results produced by the grey models based on the two construction methods are then compared and analyzed. The results show that prediction of stay time length is possible to certain degree while the students learn knowledge on web pages.

Sharing learning activity in and cross various systems is a prospective approach to implement OLP (Open Learning Process), a paradigm to share the knowledge generated in the learning process. The question is that the systems supporting teaching and learning are always separated and isolated. To implement OLP, firstly a uniform description of learning activity and a protocol to share learning activity in and cross systems are necessary. Secondly, a systematic framework to support learning activity description is also important. In this research, first, we show our vision of OLP by introducing metaphors such as a drop or an ocean of learning activity. Then based on the analysis of web based learning activity through Activity Theory, we propose a learning activity model to describe learning activity. Moreover, a specification of learning activity streams based on Activity Streams specification is introduced. Finally, a prototype system supporting learning activity streams is implemented based on UPS (Ubiquitous Personal Study) platform.

Antipatterns are mechanisms that describe how to arrive at a good (refactored) solution from a fallacious solution that has negative consequences. These mechanisms are used in a variety of computer science topics and although their integration in teaching and computer science curriculum has been proposed, the development of an e-learning system using antipatterns, still remains an open issue. Previous work has proposed the use of WebProtege, a Web-based environment that allows collaborative editing as well as annotation and voting of both components and changes of the antipattern ontology. This ontology has been implemented as the knowledge base of SPARSE, an intelligent system that uses semantic web tools and techniques in order to detect the antipatterns that exist in a software project. In this paper, we leverage this semantic web technology and the formalism of ontology in order to propose a peer-production based e-learning system for the electronically supported learning of antipatterns. We illustrate how this Web-based system can transfer antipattern knowledge using an e-learning scenario as an example.

In digital repositories around the globe there is an abundance of available resources like learning objects, learning designs, teaching methods, and assessments. Building web-based learning opportunities by exploiting these resources is a complex endeavor with controversial motivational underpinnings. It requires provision of support for accessing, using and packaging the resources with interoperable, usable and user-friendly tools and within a trustworthy environment. In this paper, we address the interoperability issue by implementing tool support for the process of designing learning opportunities based on existing educational resources in a tool called OpenGLM. We conducted a qualitative end-user evaluation based on an instructional design task. Addressing the motivational underpinnings, we identify a number of challenges and opportunities to adoption of formal instructional modeling: on the individual level the main challenge is the lack of motivation to share good-practice examples; on the institutional level the key to harness the opportunities lies in the provision of support measures and quality control.

This paper deals with

PépiMep

, a diagnosis system in school algebra. Our proposal to evaluate the students’ open-ended answers is based on a mixed theoretical and empirical approach. First, researchers in Math Education list different types of anticipated patterns of answers and the way to evaluate them. Then, this information is stored in an XML file used by the system to match a student’s input with an anticipated answer. Third, as it is impossible to anticipate every student’s answer, the system can improve: when an unknown form is detected, it is added to the XML file after expert inspection. Results from testing 360 students showed that, in comparison with human experts,

PépiMep

(1) was very effective in recognizing the different types of solutions when students’ input was an algebraic expression (2) but was less effective when students entered a reasoned response expressed by a mix of algebraic expressions and natural language utterances.

Students’ interest for science and technology studies has been decreasing during the last decade, to the profit of careers with more social impact or economic benefit. While the economic aspect is related to industrial policies and out of reach for university curriculum designers, problem-based learning (PBL) can be a solution to renew the students’ interest for science. This work presents an example of curriculum design at the master level where the theoretical classes of systems, control and information technologies (SCIT) are illustrated with PBL-oriented labs and projects related to a topic with high social impact: environmental sustainability. The focus of the paper is on practice and experience sharing; the general approach to motivate the need for SCIT in sustainable and green development is presented, along with some specific projects carried in the master program. The perspective of relying on Web 2.0 tools to improve communication and collaboration in the PBL framework is also discussed.

This paper presents the design of a remote laboratory platform using web-based technologies for radio frequency (RF), microwave measurements and their analysis. The developed RF remote laboratory system is easy to operate using web-based graphical user interfaces in controlling the test equipments and displaying the test results. The various measurement and analysis engines for RF devices, circuits, and systems are developed using graphical language, LabView. The remote RF laboratory system with the developed user interfaces and engines is used in education and research on RF engineering. The developed system can increase efficiency of education and research on RF engineering cost-effectively by providing similar environment with an actual laboratory using web-based technologies.

Students learn more and better when different educational theories are applied successfully in practice. We believe that competition among students can make the learning process much more enjoyable and motivating if the educational competition experience is conveniently designed, and other educational theories are combined with competition properly. In this paper, we analyze the main design decisions of a computer based competition assessment tool (named ISCARE, Information System for Competition during pRoblem solving in Education) that have educational implications, connecting these decisions with the correspondent educational justification.

Sharing resources and information on Internet has become an important activity for education. The MINE Registry, a branch of distributed repository, inherited the architecture of CORDRA has been developed for storing and sharing Learning Objects. Following the usage experiences, especially those being utilized to generate the lecture, the interaction structure is defined to clarify the relationships among Learning Objects. The methods to social network analysis are applied to quantify the implicit correlations and to evaluate the interdependency. In addition, an intelligent mining algorithm is proposed to explore the developed interaction structure and automatically generates lecture templates corresponding to the query criteria. The concentration of this study is to facilitate the complex and time-consuming process of creating lectures through a simple search mechanism. The implemented system has demonstrated the preliminary results and the feasibility are also revealed by the evaluation results.

In this paper, we describe a Web log mining approach to recommend learning items for each active learner based on the learner’s historical learning track. The proposed method is composed of three parts: discovering content-related item sets by Collaborative Filtering (CF), applying the item sets to Sequential Pattern Mining (SPM) and generating sequential pattern recommendations to learners. Different from other recommendation strategies which use CF or SPM separately, this paper combines the two algorithms together and makes some optimizations to adapt them for E-learning environments. Experiments are conducted for the evaluation of the proposed approach and the results show good performance of it.

CROKODIL is a platform for the support of collaborative resource-based learning with Web resources. It enables the building of learning communities in which learners annotate their relevant resources using tags. In this paper, we propose the use of automatically generated large-scale taxonomies in different languages to cope with two challenges in CROKODIL: The multilingualism of the resources, i.e. web resources are in different languages and the connectivity of the semantic network, i.e. learners do not tag resources on the same topic with identical tags. More specifically, we describe a set of features that can be used for detecting hyponymy relations from the category system of Wikipedia.

Association Link Network (ALN) is proposed to establish association relations among Web resources, aiming at extending the hyperlink-based Web to an association-rich network and effectively supporting Web intelligence activities, such as Web-based learning. However, it is difficult to build the ALN one-off by direct computing since the huge number and quickly increasing learning resources on the Web. Thus, how to rapidly and accurately acquire the association relations between the new coming and existing learning resources has become a challenge in the incrementally building process of ALN. In this paper, a new algorithm is developed for incrementally updating ALN to cater for the dynamic management of learning resources increasing with time.

In the past years learning has evolved from face-to-face to computer-supported learning, and we are now entering yet a new phase. The (r)evolution that yielded the knowledge transforming the Web 1.0 into Web 2.0 is now coming to e-learning contexts. Social media are the technologies most widely used to share educational contents, to find colleagues, discussion groups, and so on. But while in the Web 1.0 the most “time-spending” activity was to find suitable learning content, in the Web 2.0 era the search process is focused on different types of resources. This paper proposes a recommendation method that, by using a clustering algorithm, is able to support users during the selection steps. The recommendation is based on the tags defined by the network learners and the items to be recommended include not only contents but also social connections that could enrich the user’s learning process.