Interactive Videoconferencing for collaborative learning at a distance in the school of 21st century: A case study in elementary schools in Greece

The aim of this paper is to present the design, implementation and evaluation of the methodology which focuses on the pedagogical utilization of Interactive Videoconferencing (IVC) in the contemporary elementary school.     

Analyzing collaborative knowledge construction: multiple methods for integrated understanding

Documenting collaborative knowledge construction is critical for research in computer-supported collaborative learning. Because this is a multifaceted phenomenon, mixed methods are necessary to construct a good understanding of collaborative interactions, otherwise there is a risk of being overly reductionistic. In this paper I use quantitative methods of verbal data analysis, qualitative analysis, and techniques of data representation to characterize two successful knowledge building interactions from a sociocultural perspective. In the first study, a computer simulation helped mediate the interaction and in the second, a student-constructed representation was an important mediator. A fine-grained turn-by-turn analysis of the group discussions was supplemented with qualitative analysis of larger units of dialogue. In addition, chronological representations of discourse features and tool-related activity were used in study 2 to gain an integrated understanding of how a student-generated representation mediated collaborative knowledge construction. It is only by mixing methods that collaborative knowledge construction can be well characterized.     

Mining LMS data to develop an “early warning system” for educators: A proof of concept

Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international research project investigating which student online activities accurately predict academic achievement. Analysis of LMS tracking data from a Blackboard Vista-supported course identified 15 variables demonstrating a significant simple correlation with student final grade. Regression modelling generated a best-fit predictive model for this course which incorporates key variables such as total number of discussion messages posted, total number of mail messages sent, and total number of assessments completed and which explains more than 30% of the variation in student final grade. Logistic modelling demonstrated the predictive power of this model, which correctly identified 81% of students who achieved a failing grade. Moreover, network analysis of course discussion forums afforded insight into the development of the student learning community by identifying disconnected students, patterns of student-to-student communication, and instructor positioning within the network. This study affirms that pedagogically meaningful information can be extracted from LMS-generated student tracking data, and discusses how these findings are informing the development of a customizable dashboard-like reporting tool for educators that will extract and visualize real-time data on student engagement and likelihood of success.     

Using Jigsaw and Case Study for supporting online collaborative learning

In CSCL contexts (Computer Supported Collaborative Learning) collaborative activities with different levels of structuredeness are often used to foster discussion and exchange among students and enhance collaboration. In this field the debate around whether and to what extent it is useful to structure the activities proposed to students, is still very lively. In this paper two collaborative activities are explored, namely a simple Case Study (moderate level of structuredness) and the Case Study joint with the Jigsaw (higher level of structuredness). Starting from the analysis of the interactions occurred among students of two real online courses performing these activities, it is possible to identify strong points and weaknesses of the two activities, so to draw some reflections on the impact of structuredness on the collaborative learning process.     

An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization

One goal of collaborative learning is to maximize the learning performance of all participating students. In order to achieve this aim, the first step is to consider how to assist instructors in forming well-structured collaborative learning groups with a good work atmosphere to promote successful outcomes for all members. Generally, understanding levels and interests of students are two grouping criteria that are usually considered by practicing instructors. Nevertheless, when the instructors face a large number of students, simultaneously considering the two grouping criteria to form the students in an appropriate collaborative learning context is almost impossible. To address this problem, this study formulates a group composition problem to model the formation of collaborative learning groups that satisfy the two grouping criteria. Moreover, this study is based on a novel approach called particle swarm optimization (PSO) to propose an enhanced PSO (EPSO) for composing well-structured collaborative learning groups. In addition, the experimental results have demonstrated that the proposed approach is an applicable and robust method that can aid instructors in planning different kinds of collaborative learning processes.     

Contextual perspective in analysing collaborative knowledge construction of two small groups in web-based discussion

This paper presents a methodology designed to explore the role of context in collaborative knowledge construction activity in asynchronous web-based discussion. The discussions of two student groups participating in a web-based teacher education course were compared. The comparison aimed to highlight the differences and similarities between the groups’ knowledge construction activity through studying the thematic structure, communicative functions and contextual resources used in their discussions. The results indicated that the different backgrounds of the two student groups influenced the way context was created and interpreted, and how meanings were negotiated. The differences and similarities between the groups’ activity illuminated the situated and mediated nature of learning. The possibilities of the methodology used in this study for evaluating collaborative knowledge construction in context are also discussed.     

Authority and convergence in collaborative learning

Teachers and students have established social roles, norms and conventions when they encounter Computer-Supported Collaborative Learning (CSCL) systems in the classroom. Authority, a major force in the classroom, gives certain people, objects, representations or ideas the power to affect thought and behavior and influences communication and interaction. Effective computer-supported collaborative learning requires students and teachers to change how they understand and assign authority. This paper describes two studies in which students' ideas about authority led them to converge on what they viewed as authoritative representations and styles of representation too early, and the early convergence then hindered their learning. It also describes a third study that illustrates how changes to the CSCL system CAROUSEL (Collaborative Algorithm Representations Of Undergraduates for Self-Enhanced Learning) improved this situation, encouraging students to create representations that were unique, had different styles and emphasized different aspects of algorithms. Based on this research, methods to help students avoid premature convergence during collaborative learning are suggested.     

Group mirrors to support interaction regulation in collaborative problem solving

Two experimental studies test the effect of group mirrors upon quantitative and qualitative aspects of participation in collaborative problem solving. Mirroring tools consist of a graphical representation of the group’s actions which is dynamically updated and displayed to the collaborators. In addition, metacognitive tools display a standard for desirable behavior. Results show that a mirroring tool did not substantively affect the behavior of subjects while a metacognitive tool led to increased participation in dialogue, including more frequent and precise planning.     

Tracing the development of teacher knowledge in a design seminar: Integrating content,pedagogy and technology

Effective technology integration for teaching subject matter requires knowledge not just of content, technology and pedagogy, but also of their relationship to each other. Building on Schulman’s [Schulman, L. S. (1987). Knowledge and teaching: foundations for a new reform. Harvard Educational Review, 57(1), 1–22] concept of pedagogical content knowledge, we introduce a framework for conceptualizing Technological Pedagogical Content Knowledge—TPCK [Mishra, P., Koehler, M.J., (in press). Technological pedagogical content knowledge: A new framework for teacher knowledge. Teachers College Record]. We report the results of a semester-long investigation of the development of TPCK during a faculty development design seminar, whereby faculty members worked together with masters students to develop online courses. Quantitative discourse analysis of 15 weeks of field notes for two of the design teams show participants moved from considering technology, pedagogy and content as being independent constructs towards a richer conception that emphasized connections among the three knowledge bases. Our analyses suggests that developing TPCK is a multigenerational process, involving the development of deeper understandings of the complex web of relationships between content, pedagogy and technology and the contexts in which they function. Pedagogic, pragmatic, theoretical, and methodological contributions are discussed.     

Predicting college students’ online information searching strategies based on epistemological,motivational, decision-related,and demographic variables

This study examines the extent to which epistemological, motivational, decision-related, and demographic variables predict college students' use of online information searching strategies (behavioural, procedural, and metacognitive strategies). The participants included preservice teachers (N = 538) from 13 universities in different parts of Turkey. Stepwise multiple regression analyses were conducted to identify the variables predicting each online information searching strategy. The results revealed that online information searching strategies were best predicted by epistemological beliefs and then decision-making styles, web search experience, and goal orientations. Students who had advanced epistemological beliefs in speed of learning tended to have better behavioural, procedural, and metacognitive strategies, while students having naive epistemological beliefs in ability to learn had lower level online searching strategies. Students having more web search experience had better online searching strategies. Additionally, as the level of students’ mastery-approach goals increases, the use of procedural and metacognitive domain strategies increase as well, while the increase in the level of mastery-avoidance goals were related to the use of less behavioural domain strategies. Finally, students having rational decision styles were more likely to use higher levels of online information searching strategies, while students with avoidant styles tended to use less behavioural and procedural domain strategies.     

Rainbow: A framework for analysing computer-mediated pedagogical debates

In this paper we present a framework for analysing when and how students engage in a specific form of interactive knowledge elaboration in CSCL environments: broadening and deepening understanding of a space of debate. The framework is termed “Rainbow,” as it comprises seven principal analytical categories, to each of which a colour is assigned, thus enabling informal visualisation by the analyst of the extent to which students are engaging in interaction relating to potential achievement of its pedagogical goal. The categories distinguish between activities that are part of the prescribed assignment and activities that are not, and between task-focused and non-task-focused activities. Activities focused on managing the interaction itself are distinguished from argumentative interaction. Notably, an operational definition of what it means to broaden and deepen understanding in this case is also provided here. The functional Rainbow analysis is complemented by an analysis of topics and subtopics that enables identification of one form of conceptual deepening of the question. In comparison with existing analysis techniques, Rainbow synthesises much of what is known into a single framework, with a broad theoretical base. The usability and educational relevance of the framework has been validated experimentally across a variety of collaborative learning tasks and communication media. Possible and actual extensions to the framework are discussed, with respect to additional CSCL tools, domains and tasks. The research reported here was carried out within the SCALE project (Internet-based intelligent tool to Support Collaborative Argumentation-based Learning in secondary schools, project n° IST-1999–10664, March 2001 – February 2004), funded by the European Community under the ‘Information Societies’ Technology’ (IST) Programme. Information on the project can be found at: , or     

Visual attention for solving multiple-choice science problem: An eye-tracking analysis

This study employed an eye-tracking technique to examine students’ visual attention when solving a multiple-choice science problem. Six university students participated in a problem-solving task to predict occurrences of landslide hazards from four images representing four combinations of four factors. Participants’ responses and visual attention were recorded by an eye tracker. Participants were asked to think aloud during the entire task. A 4 (options) × 4 (factors) repeated measures design, two paired t-tests and effect sizes analyses were conducted to compare the fixation duration between chosen and rejected options and between relevant and irrelevant factors. Content analyses were performed to analyze participants’ responses and think aloud protocols and to examine individual’s Hot Zone image. Finally, sequential analysis on fixated LookZones was further utilized to compare the scan patterns between successful and unsuccessful problem solvers. The results showed that, while solving an image-based multiple-choice science problem, students, in general, paid more attention to chosen options than rejected alternatives, and spent more time inspecting relevant factors than irrelevant ones. Additionally, successful problem solvers focused more on relevant factors, while unsuccessful problem solvers experienced difficulties in decoding the problem, in recognizing the relevant factors, and in self-regulating of concentration. Future study can be done to examine the reliability and the usability of providing adaptive instructional scaffoldings for problem solving according to students’ visual attention allocations and transformations in a larger scale. Eye-tracking techniques are suggested to be used to deeply explore the cognitive process during e-learning and be applied to future online assessment systems.     

Prototyping a collaborative learning tool for nurses

Abstract  A Nurse Collaborative Learning (Nurse Co-Learn) tool was designed and developed to help the Department of Nursing to introduce Information Technology to undergraduate nursing students. The MUCH (Many Using and Creating Hypermedia) system was used as the basis for this tool. The Nurse Co-Learn tool supported coordination between clinic personnel. Students were required to take on a role in the clinic, and to perform the relevant tasks for that role. Through participating in this role-playing game students learned word processing, database and spreadsheet skills, whilst performing authentic tasks. Three prototypes were developed and trialled; this paper highlights the features of the prototypes and the responses of the users.     

The learning kit project: Software tools for supporting and researching regulation of collaborative learning

Computer-supported collaborative learning (CSCL) is a dynamic and varied area of research. Ideally, tools for CSCL support and encourage solo and group learning processes and products. However, most CSCL research does not focus on supporting and sustaining the co-construction of knowledge. We identify four reasons for this situation and identify three critical resources every collaborator brings to collaborations that are underutilized in CSCL research: (a) prior knowledge, (b) information not yet transformed into knowledge that is judged relevant to the task(s) addressed in collaboration, and (c) cognitive processes used to construct these informational resources. Finally, we introduce gStudy, a software tool designed to advance research in the learning sciences. gStudy helps learners manage cognitive load so they can re-assign cognitive resources to self-, co-, and shared regulation; and it automatically and unobtrusively traces each user′s engagement with content and the means chosen for cognitively processing content, thus generating real-time performance data about processes of collaborative learning.     

Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multi-dimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in.

The influence of students and teachers characteristics on the efficacy of face-to-face and computer supported collaborative learning

In this paper we compared the efficacy of face-to-face and computer supported collaborative learning (CSCL) in increasing academic knowledge and professional competences. We also explored how students’ personality characteristics and learning strategies and teachers’ characteristics were associated with better learning outcomes in online or face-to-face contexts. One hundred and seventy students participated in 10 community psychology seminars, five online and five face-to-face. Academic and professional learning increased for participants in both settings. Tutors’ characteristics did not influence students’ learning. Students who performed better in online and in face-to-face contexts differed in some psychological variables and in their learning strategies. Overall results show that asynchronous collaborative learning online can increase professional competences normally learnt only in small face-to-face educational settings, and that CSCL can be used to provide innovative educational opportunities that fit particular needs of students with low anxiety, high problem solving efficacy, who have time management problems in their learning strategies.     

Visualizing the sequential process of knowledge elaboration in computer-supported collaborative problem solving

This case study illustrates the sequential process of the joint and individual knowledge elaboration in a computer-supported collaborative learning (CSCL) environment. The case comprised an Internet-based physics problem-solving platform. Six Dutch secondary school students (three males, three females) participated in the three-week experiment. They were paired based on self-selection. Each dyad was asked to collaborate on eight moderately structured problems concerning Newtonian mechanics. Their online interactions, including their textual and pictorial messages, were categorized and sequentially plotted. The three dyads showed three different collaboration patterns in terms of joint and individual knowledge elaboration.     

Computer-assisted learning for mathematical problem solving

Previous computer-assisted problem-solving systems have incorporated all the problem-solving steps within a single stage, making it difficult to diagnose stages at which errors occurred when a student encounters difficulties, and imposing a too-high cognitive load on students in their problem solving. This study proposes a computer-assisted system named MathCAL, whose design is based on four problem-solving stages: (1) understanding the problem, (2) making a plan, (3) executing the plan and (4) reviewing the solution. A sample of one hundred and thirty fifth-grade students (aged 11 years old) completed a range of elementary school mathematical problems and empirically demonstrated. The results showed MathCAL to be effective in improving the performance of students with lower problem solving ability. This evaluation allowed us to address the problem of whether the assistances in various stages help students with their problem solving. These assistances improve students’ problem-solving skills in each stage.     

Wiki as a collaborative writing tool in teacher education: Evaluation and suggestions for effective use

Wiki technology provides new opportunities to foster collaborative writing in teacher education. To empirically evaluate the level of collaborative writing in a wiki-based environment, this article used three methods and their combination. The first method was the history function that records all students’ actions, enabling to trace all changes made in the wikis. The actions were analyzed in terms of number and percentage of contribution using a taxonomy categorized by 10 editorial types. The second method examined comments posted on the wiki discussion page to evaluate the level of collaboration. The third method provided feedback on the level of collaboration by means of peer assessment. The results show important differences in the types of contributions across the categories investigated. The results also reveal that the level of collaborative writing was lower than expected. Possible factors that may influence wiki-based collaborative writing are discussed. Finally, suggestions for effective use of wikis as collaborative writing tools in teacher education conclude the article.     

How ontologies are made: Studying the hidden social dynamics behind collaborative ontology engineering projects

Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to their construction is critical not only in understanding but consequently also in evaluating the ontologies. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.