Peter Reimann

Time is precious: Variable- and event-centred approaches to process analysis in CSCL research

Although temporality is a key characteristic of the core concepts of CSCL—interaction, communication, learning, knowledge building, technology use—and although CSCL researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order. This is particularly problematic when collaboration and learning processes are studied in groups that work together over weeks, and months, as is often the case. The quantitative method dominant in the social and learning sciences—variable-centred variance theory—is of limited value for studying change on longer time scales. We introduce the event-centred view of process as a more generally applicable approach, not only for quantitative analysis, but also for providing closer links between qualitative and quantitative research methods. A number of methods for variable- and event-centred analysis of process data are described and compared, using examples from CSCL research. I conclude with suggestions on how experimental, descriptive, and design-oriented research orientations can become better integrated.

MOOCs: So Many Learners, So Much Potential ...

Massive open online courses (MOOCs) have exploded onto the scene, promising to satisfy a worldwide thirst for a high-quality, personalized, and free education. This article explores where MOOCs fit within the e-learning and Artificial Intelligence in Education (AIED) landscape.

Collaborative Writing Support Tools on the Cloud

Academic writing, individual or collaborative, is an essential skill for todays graduates. Unfortunately, managing writing activities and providing feedback to students is very labor intensive and academics often opt out of including such learning experiences in their teaching. We describe the architecture for a new collaborative writing support environment used to embed such collaborative learning activities in engineering courses. iWrite provides tools for managing collaborative and individual writing assignments in large cohorts. It outsources the writing tools and the storage of student content to third party cloud-computing vendors (i.e., Google). We further describe how using machine learning and NLP techniques, the architecture provides automated feedback, automatic question generation, and process analysis features.

Saying the wrong thing: improving learning with multimedia by including misconceptions

In this study, 364 first-year physics students were randomly assigned to one of four online multimedia treatments on Newtons First and Second Laws of Motion: (1) the ‘Exposition’, a concise lecture-style presentation; (2) the ‘Extended Exposition’, the Exposition with additional interesting information; (3) the ‘Refutation’, the Exposition with common misconceptions explicitly stated and refuted; or (4) the ‘Dialogue’, a student–tutor discussion of the same material as in the Refutation. Students were tested using questions from mechanics conceptual inventories before and after watching the multimedia treatments. Results show the Refutation and Dialogue produced the greatest learning gains, with effect sizes of 0.79 and 0.83, respectively, compared with the Exposition. Students with low prior knowledge benefited most, however high prior knowledge learners were not disadvantaged by the misconception-based approach. The findings suggest that online multimedia can be greatly improved, promoting conceptual change in students with all levels of experience, by including a discussion of misconceptions.

The role of self‐explanation in learning to use a spreadsheet through examples

This papers describes an exploratory study into the early phase of getting to know end-user software during which users make use of a variety of information resources, including the user interface/program itself, manuals, on-line help, examples provided in the manuals and other sources. In particular, how do novices make use of the worked-out examples often provided in manuals and during training? Building on earlier research on the self-explanation effect, thinking aloud data from 10 participants were analysed to see how examples were studied and how they were used during problem solving. Important effects of self-explaining comparable to findings in other domains were found in this study. For instance, those participants who self-explain with the goal to discover meaning prove to be better problem solvers than those who do not self-explain or who focus more on syntactical aspects of examples.

Glosser: Enhanced Feedback for Student Writing Tasks

We describe Glosser, a system that supports students in writing essays by 1) scaffolding their reflection with trigger questions, and 2) using text mining techniques to provide content clues that can help answer those questions. A comparison with other computer generated feedback and scorings systems is provided to explain the novelty of the approach. We evaluate the system with Wiki pages produced by postgraduate students as part of their assessment.

Design-Based Research

Design-based research, with the design experiment as its main practical method, can be characterised as an inter-disciplinary ‘mixed-method’ research approach conducted ‘in the field’ that serves applied as well as theory-building purposes. Substantial progress has been made over recent years in articulating the methodological and epistemological basis for design-based research and in developing it into a teachable method. This chapter delineates these lines of development and provides a short overview of how a prototypical design study is conducted. It identifies and problematises the notions of design and design methods arguing that they need further conceptual development and integration with the methodological foundation.

e-Research and learning theory: What do sequence and process mining methods contribute?

This paper discusses the fundamental question of how data-intensive e-research methods could contribute to the development of learning theories. Using methodological developments in research on self-regulated learning as an example, it argues that current applications of data-driven analytical techniques, such as educational data mining and its branch process mining, are deeply grounded in an event-focused, ontologically flat view of learning phenomena. These techniques provide descriptive accounts of the regularities of events, but have limited power to generate theoretical explanations. Building on the philosophical views of critical realism, the paper argues that educational e-research needs to adopt more nuanced ways for investigating and theorising learning phenomena that could provide an account of the mechanisms and contexts in which those mechanisms are realised. It proposes that future methodological extensions should include three main aspects: (1) stratified ontological frameworks, (2) multimodal data collection and (3) dynamic analytical methods. [ABSTRACT FROM AUTHOR]

Multimedia learning: beyond modality

The technical means to develop informational and instructional resources are now at everybody’s fingertips (e.g., by using MS PowerPoint, the most widely used multimedia and instructional “authoring tool”, although by design only a presentation (sic!) tool). It also has become very easy to distribute multimedia materials: one does not need to press CD-ROMs and ship them via mail, all that is needed is to upload resources to a web server; modern browsers can render all kinds of media, from text and pictures to animated 3D models. Having the hurdle of technical and logistical problems moved out of the way, issues of learning and didactics become more prominent again: how does multimedia learning work? Under what conditions does it help to present content with multiple media? How can students become engaged in active learning, in interacting with media? What are the well-founded (general) design principles? The contributions in this Special Issue address these and a number of related questions in great depth and with scientific rigor. All of the papers make important contributions not only to current issues in multimedia learning research, but also result in insights that are of relevance for practical applications—for designing multimedia-based instructional messages and learning environments. It is notoriously difficult to write for both purposes in a single paper— to foster research and to contribute to instructional practice. Most of the contributions speak primarily to the researcher. Because I find it important that readers with an interest in instructional design can profit from this Special Issue as well, one objective for this commentary is to identify and summarize the instructional messages contained in the research papers. My second objective is to try to identify central theoretical issues and to speculate upon further research directions. In order to reach these objectives, I will first step through the individual contributions and state what I see as their central points. In doing so, I shall not follow the exact sequence of articles. Of all the papers, the one by Richard Mayer is most clearly directed towards the

Designs for Learning Environments of the Future

3, 9, 24, 28, 120, 126, 127, 135, 151, 173, 174, 189, 193, 194, 240, 252, 256, 279 Affordances infrastructural, 238 representational, 8–9 technological, 7, 233 Agent-based modeling, 3, 17–56 Aggregation, 23, 207, 208 Allele, 62, 67, 69, 76, 78, 84, 85 Analysis, 5, 18, 25, 36–51, 53, 72, 78, 81, 85, 94, 98, 101, 115, 128, 147, 150–153, 155, 157, 162, 173–176, 178, 180–182, 184, 197–199, 211, 215, 218–220, 224–227, 242, 277–279 Animation. See Representation, animation Annotations, 151, 152 Argumentation, 3, 149, 150, 153, 215, 240, 247, 249–251, 258 Artifact creation, 1, 92, 144–146, 148, 150, 153, 207, 213, 272 Artificial intelligence, 3, 119 Assessment. See also Evaluation embedded, 61, 63, 76, 80 paper-and-pencil test, 283 Authenticity, 89, 93, 95, 104, 115, 147, 158, 218, 283 Authoring, 6, 30, 44, 82, 83, 91, 121, 132, 149, 150, 153, 170, 171, 173–175, 192, 207, 208, 215, 224, 227 Automap, 174–175, 178, 180