scholarly journals Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics

2020 ◽  
Vol 34 (4) ◽  
pp. 890-905 ◽  
Author(s):  
Dirk T. Tempelaar ◽  
Bart Rienties ◽  
Quan Nguyen
Keyword(s):  
Individual Differences ◽  
Learning Analytics ◽  
Worked Examples

Assessment and Evaluation of Students' Performance Without Learning Analytics

2018 ◽  
pp. 96-112
Author(s):  
Sushant Zesu
Keyword(s):  
Academic Achievement ◽  
Individual Differences ◽  
Learning Analytics ◽  
Its Region ◽  
Assessment And Evaluation ◽  
Traditional System ◽  
Student’S Performance ◽  
In The Beginning

Humans have constantly tried to assess and measure individual differences. But in the beginning of the prevailing century there has been a fast advancement on this discipline. The old methods of assessment have been defective and unreliable. In their area, reliable and standardized equipment of assessment have now been constructed. Every teacher desires that s/he must by some means assess the academic achievement of the student's performance that s/he has taught. This is why examinations are every day and use every tool of training. But today, the traditional system of exam is taken into consideration. Ruled defective and in its region, a new tool is deliberated and taken into consideration as goal related to real global duties. In this chapter, the author explains various types of examinations.

scholarly journals Temporal autocorrelation: a neglected factor in the study of behavioral repeatability and plasticity

2019 ◽  
Author(s):  
David J Mitchell ◽  
Antoine M Dujon ◽  
Christa Beckmann ◽  
Peter A Biro
Keyword(s):  
Individual Differences ◽  
Individual Variation ◽  
Repeated Measures ◽  
Poecilia Reticulata ◽  
Energy Levels ◽  
Worked Examples ◽  
Reaction Norm ◽  
Physiological Data ◽  
Temporal Autocorrelation ◽  
Internal States

Abstract Quantifying individual variation in labile physiological or behavioral traits often involves repeated measures through time, so as to test for consistency of individual differences (often using repeatability, “R”) and/or individual differences in trendlines over time. Another form of temporal change in behavior is temporal autocorrelation, which predicts observations taken closely together in time to be correlated, leading to nonrandom residuals about individual temporal trendlines. Temporal autocorrelation may result from slowly changing internal states (e.g., hormone or energy levels), leading to slowly changing behavior. Autocorrelation is a well-known phenomenon, but has been largely neglected by those studying individual variation in behavior. Here, we provide two worked examples which show substantial temporal autocorrelation (r > 0.4) is present in spontaneous activity rates of guppies (Poecilia reticulata) and house mice (Mus domesticus) in stable laboratory conditions, even after accounting for temporal plasticity of individuals. Second, we show that ignoring autocorrelation does bias estimates of R and temporal reaction norm variances upwards, both in our worked examples and in separate simulations. This bias occurs due to the misestimation of individual-specific means and slopes. Given the increasing use of technologies that generate behavioral and physiological data at high sampling rates, we can now study among- and within-individual changes in behavior in more detailed ways, including autocorrelation, which we discuss from biological and methodological perspectives and provide recommendations and annotated R code to help researchers implement these models on their data.

The challenge of accounting for individual differences in folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Benjamin C. Ruisch ◽  
Rajen A. Anderson ◽  
David A. Pizarro
Keyword(s):  
Individual Differences ◽  
Individual Variation ◽  
Political Ideologies ◽  
Economic Beliefs ◽  
Existing Data

AbstractWe argue that existing data on folk-economic beliefs (FEBs) present challenges to Boyer & Petersen's model. Specifically, the widespread individual variation in endorsement of FEBs casts doubt on the claim that humans are evolutionarily predisposed towards particular economic beliefs. Additionally, the authors' model cannot account for the systematic covariance between certain FEBs, such as those observed in distinct political ideologies.

Individual differences, social attention, and the history of the social motivation hypotheses of autism

2019 ◽  
Vol 42 ◽  
Author(s):  
Peter C. Mundy
Keyword(s):  
Individual Differences ◽  
Early Development ◽  
Emotional Development ◽  
Social Motivation ◽  
Social Attention ◽  
Social Emotional Development ◽  
Social Emotional ◽  
Precise Understanding ◽  
The Social ◽  
History Of

Abstract The stereotype of people with autism as unresponsive or uninterested in other people was prominent in the 1980s. However, this view of autism has steadily given way to recognition of important individual differences in the social-emotional development of affected people and a more precise understanding of the possible role social motivation has in their early development.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

Inter-individual variation shapes the human microbiome

2019 ◽  
Vol 42 ◽  
Author(s):  
Emily F. Wissel ◽  
Leigh K. Smith
Keyword(s):  
Individual Differences ◽  
Individual Variation ◽  
Human Microbiome ◽  
Individual Variability ◽  
Microbiota Composition ◽  
Target Article ◽  
Microbiome Research

Abstract The target article suggests inter-individual variability is a weakness of microbiota-gut-brain (MGB) research, but we discuss why it is actually a strength. We comment on how accounting for individual differences can help researchers systematically understand the observed variance in microbiota composition, interpret null findings, and potentially improve the efficacy of therapeutic treatments in future clinical microbiome research.

Why Speech-Language Clinicians Should Be in the Classroom

1991 ◽  
Vol 22 (4) ◽  
pp. 277-277 ◽  
Author(s):  
Sharon L. Wadle
Keyword(s):  
Individual Differences ◽  
Task Analysis ◽  
Regular Classroom ◽  
Classroom Setting ◽  
Therapy Room

Lack of training is only an excuse for not collaborating outside of the therapy room. With our present training, speech-language clinicians have many skills to share in the regular classroom setting. This training has provided skills in task analysis, a language focus, an appreciation and awareness of individual differences in learning, and motivational techniques.

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