Novel cerebrospinal fluid biomarkers of glucose transporter type 1 deficiency syndrome: Implications beyond the brain's energy deficit

We used next-generation metabolic screening to identify new biomarkers for improved diagnosis and pathophysiological understanding of glucose transporter type 1 deficiency syndrome (GLUT1DS), comparing metabolic cerebrospinal fluid (CSF) profiles from 12 patients to those of 116 controls. This confirmed decreased CSF glucose and lactate levels in patients with GLUT1DS and increased glutamine at group level. We identified three novel biomarkers significantly decreased in patients, namely gluconic + galactonic acid, xylose-α1-3-glucose, and xylose-α1-3-xylose-α1-3-glucose, of which the latter two have not previously been identified in body fluids. CSF concentrations of gluconic + galactonic acid may be reduced as these metabolites could serve as alternative substrates for the pentose phosphate pathway. Xylose-α1-3-glucose and xylose-α1-3-xylose-α1-3-glucose may originate from glycosylated proteins; their decreased levels are hypothetically the consequence of insufficient glucose, one of two substrates for O-glucosylation. Since many proteins are O-glucosylated, this deficiency may affect cellular processes and thus contribute to GLUT1DS pathophysiology. The novel CSF biomarkers have the potential to improve the biochemical diagnosis of GLUT1DS. Our findings imply that brain glucose deficiency in GLUT1DS may cause disruptions at the cellular level that go beyond energy metabolism, underlining the importance of developing treatment strategies that directly target cerebral glucose uptake

22-04-2005). Dynamic Task Selection in Aviation Training

Personal and educational classroom use of this paper is allowed. Commercial use requires specific permission from the author. While the aviation domain is exemplary for its complex cognitive skills, the pace of automation steadily increases making it crucial to train people as effectively as possible. Over the last three decades training programs have evolved a strong focus on personalized dynamic whole-tasks. Adapting training to the individual student’s progress is believed to be strongly related to increased training efficiency (Salden, Paas, & van Merriënboer, 2006). Four studies investigated a variety of personalized training methods. Results confirm the hypothesis that personalized instruction can have beneficial effects for the training of complex cognitive skills. Dynamic Task Selection in Aviation Training Technical domains like chemical industry and aviation incorporate a vast amount of complex cognitive skills in high demanding working environments. Mistakes can lead to dangerous situations and high costs, yet the available training time in which the complex job skills have to be mastered, is limited. Efficient training that offers trainees a powerful learnin

The expertise reversal effect and worked examples in tutored problem solving

Prior research has shown that tutored problem solving with intelligent software tutors is an effective instructional method, and that worked examples are an effective complement to this kind of tutored problem solving. The work on the expertise reversal effect suggests that it is desirable to tailor the fading of worked examples to individual students’ growing expertise levels. One lab and one classroom experiment were conducted to investigate whether adaptively fading worked examples in a tutored problem-solving environment can lead to higher learning gains. Both studies compared a standard Cognitive Tutor with two example-enhanced versions, in which the fading of worked examples occurred either in a fixed manner or in a manner adaptive to individual students’ understanding of the examples. Both experiments provide evidence of improved learning results from adaptive fading over fixed fading over problem solving. We discuss how to further optimize the fading procedure matching each individual student’s changing knowledge level

Mental effort and performance as determinants for the dynamic selection of learning tasks in Air Traffic Control training

The differential effects of four task selection methods on training efficiency and transfer in computer-based training for Air Traffic Control were investigated. A non-dynamic condition, in which the learning tasks were presented to the participants in a fixed, predeterminedsequence, was compared to three dynamic conditions, in which learning tasks were selected on the basis of performance, mental effort, and a combination of both (i.e., mental efficiency). Using the 3-factor mental efficiency formula of Tuovinen and Paas (2004, this issue), the hypothesis that dynamic task selection leads to more efficient training than non-dynamic task selection was confirmed. However, the hypothesis that dynamic task selection based on mental efficiency leads to more efficient training than dynamic task selection based on performance or mental effort alone was not supported. The results are discussed in light of the theoretical frame work and suggestions are given for future research

Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving

Recent studies have tested the addition of worked examples to tutored problem solving, a more effective instructional approach than the untutored problem solving used in prior worked example research. These studies involved Cognitive Tutors, software designed to support problem solving while minimizing extraneous cognitive load by providing prompts for problem sub-goals, step-based immediate feedback, and context-sensitive hints. Results across eight studies in three different domains indicate that adding examples to Cognitive Tutors is beneficial, particularly for decreasing the instructional time needed and perhaps also for achieving more robust learning outcomes. These studies bolster the practical importance of examples in learning, but are also of theoretical interest. By using a stronger control condition than previous studies, these studies provide a basis for refining Cognitive Load Theory explanations of the benefits of examples. Perhaps, in addition to other reasons, examples may help simply because they more quickly provide novices with information needed to induce generalized knowledge

Metacognitive support promotes an effective use of instructional resources in intelligent tutoring

We tested whether the provision of metacognitive knowledge on how to cope with the complexity of a learning environment improved learning. In an experimental setting, high-school students (N = 60) worked through a computer-based geometry lesson either with or without metacognitive support in the form of a cue card. This cue card encouraged students to use instructional resources in the learning environment (i.e., textual and graphic representations and different help facilities) more strategically. During learning, the learners' gaze and log-file data were recorded. The metacognitive support made learning more efficient (i.e., less learning time without impairing outcomes). In addition, low-prior knowledge students developed deeper conceptual understanding. The effects on learning outcomes were mediated by reducing the non-strategic use of help facilities. Our findings suggest that a lack of metacognitive conditional knowledge (i.e., in which situation to use which help facility) can account for learning difficulty in computer-based learning environments

Worked Examples and Tutored Problem Solving:Redundant or Synergistic Forms of Support?

The current research investigates a combination of two instructional approaches, tutored problem solving and worked-examples. Tutored problem solving with automated tutors has proven to be an effective instructional method. Worked-out examples have been shown to be an effective complement to untutored problem solving, but it is largely unknown whether they are an effective complement to tutored problem solving. Further, while computer-based learning environments offer the possibility of adaptively transitioning from examples to problems while tailoring to an individual learner, the effectiveness of such machine-adapted example fading is largely unstudied. To address these research questions, one lab and one classroom experiment were conducted. Both studies compared a standard Cognitive Tutor with two example-enhanced Cognitive Tutors, in which the fading of worked-out examples occurred either fixed or adaptively. Results indicate that the adaptive fading of worked-out examples leads to higher transfer performance on delayed post-tests than the other two methods