The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cognitive and metacognitive self-regulated learning processes as they learned about the ...circulatory system with MetaTutor, a hypermedia-based intelligent tutoring system. We coded for the accuracy of detecting students’ cognitive and metacognitive processes, and examined how the computed scores related to mean evidence scores of emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted the accuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted the accuracy of taking notes, a cognitive learning strategy. These results have implications for understanding the beneficial role of negative emotions during learning with advanced learning technologies. Future directions include providing students with feedback about the benefits of both positive and negative emotions during learning and how to regulate specific emotions to ensure the most effective learning experience with advanced learning technologies.
•Mean evidence score of contempt was significantly lower than joy or anger.•Mean evidence score of anger was significantly higher than frustration.•Emotions correlated with accuracy scores, but not proportional learning gain.•Surprise negatively predicted accuracy score of feeling of knowing.•Frustration positively predicted accuracy score of notes.
We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a ...particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.
A wealth of scientifically and clinically relevant information is hidden, and potentially invalidated, when data are averaged across subjects.
There is growing interest in using neuroimaging to explain differences in human abilities and disabilities. Progress in this endeavour requires us to treat intersubject variability as data rather than noise.
Our plastic and noisy brains intrinsically change the parameterisation of each individual’s brain, providing a rich opportunity to understand differences in brain function.
Normal variability can be used to decode different neural pathways that can sustain the same task (degeneracy).
This is of paramount importance for understanding why patients have variable outcomes after damage to seemingly similar brain regions.
Experienced athletes use self-talk (ST) when challenged to monitor distress, to continue effort, and battle fatigue. The power clean, a training modality for power sports, challenges athletes to ...develop cognitive strategies to maintain performance, technique, and persist. A problem has been that ST studies have not measured perception of effort and muscle firing when ST is purposefully withheld. The purpose of this study was to compare the effects of ST to a control group during a session of power clean to fatigue. Also, multiple parameters were assessed and included perceptual, neural, and performance standards. The method of randomly assigned ST and control (CON) groups compared 24 experienced Olympic lifting men (age range 18 to 28 years). The groups completed continuous sets of power cleans for three repetitions at 85% of maximum effort with a three-minute rest in between sets until failure. The ST group was instructed to engage in organic, goal-directed self-talk (ST group) during exercise. The CON group focused on a neutral attentional focus. The results demonstrated that the ST group achieved more sets, reps, and total weight lifted (p < 0.05). Both groups had comparable increases in perceived exertion prior to fatigue (p < 0.001). Persistence (numbers of sets and repetitions) after reaching the perceptual breakpoint (RPE of “8”) was higher for the ST group (p <0.01) by 8.5 repetitions. Pain tolerance was slightly higher in the ST group as well. The ST group demonstrated lower activation in two muscle groups despite performing more work. In conclusion, ST enhanced performance by 43% once an RPE of eight was reached, resulted in 63% more repetitions, and demonstrated more efficient muscle activation patterns.
This study develops and empirically tests a conceptual model comprising eight hypotheses that focus on the impact of Augmented Reality (AR) on student academic self-efficacy. A controlled experiment ...was conducted, followed by an online questionnaire with 65 students. Partial Least Square (PLS) Structural Equation Modelling (SEM) is used to assess and test the model. The results show that student cognitive strategies impact student perception and engagement with technology and learning tasks, with AR positively impacting student academic self-efficacy in higher education. Interestingly, neither the learning space nor the students' perception of task value was shown to impact their intention to use AR. Instead, it was the characteristics of the technology itself which encouraged adoption. The study's findings reinforce the need to (1) teach and facilitate effective cognitive strategies among third level students and (2) identify and overcome negative cognitive strategies that harm student self-efficacy and engagement.
•Empirically tested conceptual model focusing on Augmented Reality (AR) and Student Academic Self-Efficacy•The characteristics of the AR application enticed the students to use it.•The learning environment nor the value students placed on the AR based activity influenced their intention to use AR.•The strategies the students used to learn influenced their confidence in their abilities and their view of the technology.•AR was also shown to increase students' academic confidence.
Analyzing the self-regulatory process of complex science learning is a serious challenge as it takes considerable time to train coders and do real-time assessment of a learner's verbatim transcript. ...Thus, the aim of this study was to investigate the potential and opportunities of artificial intelligence (AI) methods to analyze self-reporting protocols for recognizing cognitive and metacognitive strategies of self-regulated learning. Sixty-six participants were recruited to evaluate the quality of given scientific explanations while self-reporting their interpreting and reasoning processes. The self-reported protocols were further coded and categorized as cognitive or metacognitive activities for training and evaluating an AI model. Long Short-Term Memory, an AI classifier, was employed to predict the rich narrative texts expressed by learners on using strategies in complex science tasks. Quantitative analysis was conducted to evaluate the performance of the classifier. Results suggested promising accuracy/consistency between human-based and the AI classifier. In addition, two design factors, AI structure and dropout rate, did not significantly impact the performance. Qualitative examinations of discrepancies between human and AI classifier revealed that length of segments and segments including a phrase or words with temporal cues could potentially influence the accuracy of AI judgments. Overall, The AI classifier yielded a fair performance demonstrating acceptable accuracy in the prediction of cognitive or metacognitive strategies with a limited dataset for a total of merely 104 protocols from 66 participants. Our qualitative observations that attempt to explain sources of human-computer discrepancies may shed light on future improvement for AI-based methods. Implications of AI for self-regulated digital learning are discussed.
•Promising consistency between human-based and the artificial intelligence (AI) classifier.•Structures of AI and dropout rate did not significantly impact the performance.•Segments including a phrase or words with temporal cues could influence the accuracy of AI.•Predict cognitive or metacognitive strategies with a small sample size of 66 learners is feasible.
Metacognition, the ability to monitor and regulate cognitive processes, is essential for individuals with Mild Cognitive Impairment (MCI) to accurately identify their deficits and effectively manage ...them. However, previous studies primarily focused on memory awareness in MCI, neglecting other domains affected in daily life. This study aimed to investigate how individuals with MCI perceive their abilities to handle various cognitively challenging situations representing real-life scenarios and their use of compensatory strategies. Thus 100 participants were recruited, including 50 with amnestic MCI with multiple deficits (aMCI) and 50 cognitively healthy controls (HC) matched in age and education. Participants completed three metacognitive scales assessing self-perceived efficacy in everyday life scenarios and one scale evaluating use of cognitive strategies. Results indicated that aMCI participants reported significantly lower self-efficacy in memory and divided-shifted attention scenarios compared to HC. Surprisingly, no significant group differences were found in the self-reports about the use of cognitive strategies. This suggests a potential gap in understanding or applying effective strategies for compensating cognitive deficits. These findings emphasize the importance of cognitive training programs targeting metacognitive knowledge enhancement and practical use of cognitive strategies that could enhance the quality of life for individuals with MCI.
Many individuals with schizophrenia are reported to have maladaptive expression and processing of emotion. This may take the form of conscious and implicit processes. Potential regulatory processes ...underlying schizophrenia are reviewed. We aimed to estimate effect sizes, potential heterogeneity and publication bias across three areas of measurement: a range of cognitive emotion regulation strategies11Cognitive Emotion Regulation Strategies is abbreviated as CERS throughout the paper. (CERS), alexithymia and dissociation.
Data were pooled from 47 case–control studies involving measures of experiential avoidance, attentional deployment, cognitive reappraisal, emotion management, dissociation and alexithymia. All studies were rated for quality, risk of bias and publication bias.
The following effect sizes (g) were observed: emotion management: 0.96 0.77, 1.14 and cognitive reappraisal: 0.49 0.32, 0.66 were negatively associated with schizophrenia. Experiential avoidance: −0.44 −0.59, −0.29, attentional deployment −0.96 −1.18, −0.75, dissociation: −0.86 −1.13, −0.60 and alexithymia: −1.05 −1.45, −0.65 were positively associated with schizophrenia. Subgroups of dissociation and attentional deployment were also analysed. Meta-analyses revealed potential publication bias and heterogeneity in the study of CERS in schizophrenia.
A marked difference in the implementation of CERS is associated with schizophrenia compared to controls. Dissociation variables and alexithymia are also indicated and may be implicated in adaptive cognitive emotional regulation. Theoretical and research implications are discussed.
•Individuals with schizophrenia display more maladaptive use of CERS.•Dissociation and alexithymia may influence CERS in schizophrenia.•Conceptual ambiguity/overlap may exist between dissociation, alexithymia and CERS.•There was poor methodological rigour displayed in the research literature.
The ability to remember and manipulate visual information is pervasive and is associated with many cognitive abilities. Yet despite the importance of visual working memory (VWM), there is little ...consensus among researchers in the field as to which neural areas are necessary and sufficient and which models best describe its capacity. Here, we propose that an assumption that all people remember visual information in the same way has led to much contention and inconsistencies in the field. By accepting that there are multiple cognitive strategies and methods to perform a VWM task, we introduce an individual “precision” approach to the study of memory. We propose that VWM should be redefined, not by the type of stimuli used (e.g., visual) but rather by the specific mental processes (e.g., visual imagery, semantic, propositional, spatial) and the corresponding brain regions used to complete the mnemonic task. We further provide a short how-to guide for measuring different mnemonic strategies used for working memory.
The Digit Span Backwards Task Hilbert, Sven; Nakagawa, Tristan T.; Puci, Patricia ...
European journal of psychological assessment : official organ of the European Association of Psychological Assessment,
01/2015, Letnik:
31, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The "digit span backwards" (DSB) is the most
commonly used test in clinical neuropsychology to assess working memory
capacity. Yet, it remains unclear how the task is solved cognitively. The
present ...study was conducted to examine the use of visual and verbal cognitive
strategies in the DSB. Further, the relationship between the DSB and a complex
span task, based on the Simultaneous Storage and Processing
task (Oberauer et al., 2003),
was investigated. Visualizers performed better than verbalizers in the dual task
condition (rPB = .23) only when the
relevant digits were presented optically. Performance in the DSB correlated only
weakly with the complex span task in all conditions (all
τ ≤ .21). The results indicate
that the processing modality is determined by the preference for a cognitive
strategy rather than the presentation modality and suggest that the DSB measures
different working aspects than commonly used experimental working memory
tasks.
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