Sarah Manlove

Co-Lab: research and development of an online learning environment for collaborative scientific discovery learning

There are many design challenges that must be addressed in the development of collaborative scientific discovery learning environments. This contribution presents an overview of how these challenges were addressed within Co-Lab, a collaborative learning environment in which groups of learners can experiment through simulations and remote laboratories, and express acquired understanding in a runnable computer model. Co-Labs architecture is introduced and explicated from the perspective of addressing typical problem areas for students within collaborative discovery learning. From this view the processes of collaboration, inquiry, and modeling are presented with a description of how they have been supported in the past and how they are supported within Co-Labs design and tools. Finally, a research agenda is proposed for collaborative discovery learning with the Co-Lab environment.

Trends and issues of regulative support use during inquiry learning: Patterns from three studies

This paper looks across three experimental studies that examined supports designed to assist high-school students (age 15-19) with cognitive regulation of their physics inquiry learning efforts in a technology-enhanced learning environment called Co-Lab. Cognitive regulation involves the recursive processes of planning, monitoring, and evaluation during learning, and is generally thought to enhance learning gains for students. The research synthesis described in this paper examined the usage effects of a support tool called the process coordinator (PC) on learning outcomes. This tool incorporated goal-lists, hints, prompts, cues, and templates to support the cognitive regulation skills of students during a fluid dynamics task. Students were asked to produce two learning outcomes of their investigations: system dynamics models and lab reports. Results from the three studies indicated trends in frequent use of the PC for planning activities, but low usage for monitoring and evaluation. Correlational analysis revealed two trends with regard to how these regulative activities impacted learning outcomes. First, consistent positive correlations were apparent between regulative activities and lab report scores of students and second, consistent negative correlations between the use of supports and model quality scores. Trends with regard to how task complexity, time, and student prior experience impacted these findings are also presented with suggestions for future research.

Collaborative versus individual use of regulative software scaffolds during scientific inquiry learning

Scaffolds to plan, monitor, and evaluate learning within technology-enhanced inquiry and modeling environments are often little used by students. One reason may be that students frequently work collaboratively in these settings and their group work may interfere with the use of regulative supports. This research compared the use of regulative scaffolds within an inquiry and modeling environment by paired and single students. Pairs were predicted to make less use of regulative scaffolds than singles. To validate this assumption 42 high-school students worked either individually (n = 18) or in pairs (n = 12) within an inquiry learning environment. Two regulative scaffolds were used by both conditions to assist them with planning, monitoring, and evaluating their investigative efforts; a cognitive tool called the Process coordinator and a Laboratory report template. Results showed that pairs achieved significantly higher learning outcomes than individual students, and although there was a strong trend of increased regulative tool use by individual students, the frequency and duration of regulative tool use did not differ significantly between conditions. Implications of these effects for regulative scaffold design and use are discussed and suggestions for future research are advanced.

Affective Processes in Learning

In the last decade the role of emotions in education seems to have been rediscovered (Maehr, 2001). Affective processes are now recognised as playing an important role in learning. Students’ emotions, such as, enjoyment, boredom, pride, and anxiety are seen to affect achievement by influencing the student’s involvement and attitude towards learning and learning environments, which also affects how (intensively) students process and/or interpret information (for a discussion see e.g., Boekaerts, 2003; Boekaerts & Simons, 1995; Pekrun, 2005; Pekrun, Goetz, Titz, & Perry, 2002). This (renewed) attention for affect is the result of several developments. A first development is the change from teacher-directed to learner-centred approaches in education, which often involve giving more responsibility for and control over the learning process to the learners.

Issues from Neuroscience

The present volume takes educational issues as a starting point and looks at possible contributions which could be given from the point of view of cognitive neuroscience. Issues that directly arise from the neurosciences which might be of relevance for education are only described in relation to educational issues. The interested reader is referred to recent reviews and ‘opinion’ articles which take a more cognitive neuroscience stance (e.g., Ansari & Coch, 2006; Casey, Getz, & Galvan, 2008; Jolles, 2007a,b; OECD, 2002, 2007; Steinberg, 2008) and other papers, mentioned in the Introduction. Yet, two issues deserve a short elaboration, because they are pertinent to the scientific findings and directions described in earlier sections. This concerns the issues of ‘plasticity’ and ‘maturation’.

Second) Language Learning and Literacy

Literacy is incredibly complex, and a full report on the links between neuroscience and language instruction would be an undertaking all on its own. In this chapter we therefore focus on a number of focal questions: What is the neurological basis of development of literacy? Can cognitive neuroscience help to distinguish between competing models discussed in educational research? What is the role of age of acquisition in second language (L2) learning? Does early L2 learning have a negative impact on acquisition of literacy in the native language? Can late L2 learners process an L2 in a native-like way? First, we give a brief overview of educational research developments in these areas, followed by an overview of neurocognitive contributions.

Numeracy and Mathematics Learning

Because numeracy, like literacy, results from the interplay of biology and experience it is the natural domain of both cognitive neuroscience and educational science. Although there is no single agreed upon definition, numeracy implies an understanding of the concept of number and the ability to reason quantitatively. As such, it is considered the basis of both simple and complex mathematics.