The culture of education

Using digital technology to deliver content, connect learners, and enable anytime, anywhere learning is increasing, but keeping students engaged in technology-mediated learning is challenging. Instructional practices that encourage greater engagement are essential if we are to effectively use digital instructional technologies. To determine the impact of innovative instructional practices on learning, we need useful measures of student engagement. These measures should be adaptable to the unique challenges to studying technology-mediated learning, such as when students learn at a distance or in a blended learning course. In this review, we examine existing approaches to measure engagement in technology-mediated learning. We identify strengths and limitations of existing measures and outline potential approaches to improve the measurement of student engagement. Our intent is to assist researchers, instructors, designers, and others in identifying effective methods to conceptualize and measure student engagement in technology-mediated learning. Consensus is needed for the definition and operationalization of student engagement.Most technology-mediated learning research uses self-report measures of engagement.Physiological and systems data offer an alternative method to measuring engagement.More research is needed to study the role of emotional engagement in learning.More research needed to determine value of physiological and systems data.

Measuring student engagement in technology-mediated learning

This chapter summarizes two decades of research on computer-supported collaborative learning (CSCL). We first review the key idea that has emerged, namely the fact that collaboration among peers can be “designed”, that is, directly or indirectly shaped by the CSCL environment. Second, we stress the fact that affective and motivational aspects that influence collaborative learning have been neglected by experimental CSCL researchers. Finally, we point out the emergence of a new trend or new challenge: integration of CSCL activities into larger pedagogical scenarios that include multiple activities and must be orchestrated in real time by the teacher.

The Evolution of Research on Computer-Supported Collaborative Learning

Computers and the collaborative experience of learning

Despite intensive research in computer-supported collaborative learning (CSCL) over the last decade, there is relatively little research about how groups and individuals in groups engage, sustain, support, and productively regulate collaborative processes. This article examines the role of regulatory processes in collaborative learning and how CSCL environments can be used for shared regulation of learning. First, we establish the importance of regulation processes and introduce three types of regulation contributing to successful collaboration: self-, co-, and socially shared regulation of learning. Second, we illuminate two strands of seemingly diverse research that lay an important foundation for supporting and researching regulation in CSCL contexts establishing that (a) computer-based pedagogical tools used to successfully support regulation in individual learning contexts can be leveraged for collaborative task contexts, and (b) computer-based tools for supporting collaborative knowledge constructio...

New Frontiers: Regulating Learning in CSCL.

Previous studies suggest that before participants in web-based conferencing can reach deeper level interaction and learning, they have to gain an adequate level of common ground in terms of shared mutual understanding, knowledge, beliefs, assumptions, and presuppositions (Clark & Schaefer, 1989; Dillenbourg, 1999). In this paper, the purpose is to explore how participants establish and maintain common ground in order to reach deeper level interaction in case-based web discussions. The subjects in this study consisted of 68 preservice teachers and 7 mentors from 3 universities, who participated in a web-based conferencing course for 8 weeks. The written discussion data were analyzed by means of a combination of quantitative and qualitative methods. The results suggest that in order to establish common ground, it is essential that the participants, especially as fellow students, not only show evidence of their understandings through written feedback, but also provide support to their peers in their replies. Presenting questions also signals the willingness of participants to continue the discussion, which is essential for maintaining common ground.

/pdf/mechanisms-of-common-ground-in-case-based-web-discussions-in-39rlq9xny6.pdf

Mechanisms of common ground in case-based web discussions in teacher education

Computer-supported collaborative cearning (CSCL) is a recent approach to creating 1 powerful learning and communication environments in combination with 2 collaborative learn ing ideas and networked technology. Many advanced technical 3 infrastructures for fostering higher-level processes of inquiry-based interaction have 4 been developed (Edelson, Gordin, & Pea, 1999; Scardamalia & Bereiter, 1996). This 5 chapter discusses instructional support in CSCL. First, the basic processes of 6 instructional scaffolding in the context of CSCL are discussed, then relevant 7 instructional models dealing with collaborative learning are presented. The specific 8 focus is to introduce ‘basic models’ from the cooperative learning tradition to the 9 more recent inquiry models, which are applicable to CSCL. The relationship 10 between instructional support and issues of human learning and the educational 11 setting is also discussed and cases of instructional support in CSCL are presented. 12

Instructional support in CSCL

This study investigates how distributed team members evaluate their own and others’ knowledge when they engage in goal-directed activities and seek shared understanding. Twenty-three manager-level employees of a municipal organisation worked as two distributed teams for two months. Their work was supported with a visualisation tool, which was embedded in the teams’ shared www-based workspaces. After the distributed working period, the subjects were interviewed and their work in the shared workspace was traced. Qualitative analysis of the interviews showed that, in distributed collaboration, individuals use both self-evaluation and interpersonal evaluation strategies when trying to gain an awareness of others’ knowledge. The interpersonal evaluations included strategies such as assessing the expertise and knowledge of others. It is concluded that when individuals do not have situational information, eg, what others think about the content of the shared task, they tend to make personal attributions. In other words, their evaluations of the others’ knowledge focus on stable tendencies like the expertise of other individuals.

Facilitating Interpersonal Evaluation of Knowledge in a Context of Distributed Team Collaboration.

目的:コンピュータを媒介にした議論における貢献に関して生徒自らの解釈を調査 対象:15歳から16歳の高学年20人(upper elementary) 方法:CSCL(Computer Supported Collaborative Learning)に参加 Knowledge Forum、ネットワーク化された学習環境の提供 the Experience-Sampling Method (ESM、臨床)に従う questionnaireの開発 協同的知識構築プロセスにおける生徒自身の解釈 結果:生徒はいかに彼らの貢献(協同学習に関する)を解釈するのか 解釈後の活動の変化 コンピュータを媒介にした議論に対する生徒の貢献差を表象する4つのタイプ a) 積極的な貢献者 b) 非積極的な貢献者 c) 中心的にコメントを受ける者 c) 孤立された者 Intro 先行研究による理論的背景 Positive: 新しいコミュニケーション技術の適用 協同的知識構築の道具を提供、科学的議論の足場作り、仮想コミュニティの構築 Negative: 個人の貢献度の差、CSCL文脈での議論・参加の非活性化 1. Individuals’ Contribution to Social Knowledge Construction 協同的知識構築は明示的に描写することが難しい. Lipponen(2001)による,2つの主なライン:

Individual Students' Interpretations of Their Contribution to the Computer-Mediated Discussions.

Purpose – Evaluation of the knowledge that is shared among team members has been found to be a prerequisite for successful collaborative teamwork. In previous research on collaborative learning and work, shared knowledge has mainly been evaluated by researchers, and an individual's own perspective has been omitted. In this study the aim was to investigate how members of a distributed team explain knowledge that they assume to be shared and how an assessment tendency is related to these explanations.Design/methodology/approach – Two distributed teams worked for two months to solve project tasks that demanded discussions and generating new ideas. Subjective explanations of their shared knowledge were studied by means of stimulated recall interviews. The concept of assessment tendency was employed to understand the differences in these explanations. Team members' assessment tendencies were examined with the assessment scale questionnaire.Findings – Qualitative content analyses of interviews showed that expla...

The distributed team members' explanations of knowledge they assume to be shared

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Piritta Leinonen

Author Tools

Chat about Author