Supplemental Material for Motivational Profiles Across Domains and Academic Choices Within Eccles et al.’s Situated Expectancy–Value Theoretical Framework

Motivational profiles across domains and academic choices within Eccles et al.’s situated expectancy–value theoretical framework.

Developmental Psychology ◽

10.1037/dev0001250 ◽

2021 ◽

Vol 57 (11) ◽

pp. 1893-1909

Author(s):

Rebecca Lazarides ◽

Anna-Lena Dicke ◽

Charlott Rubach ◽

Elisa Oppermann ◽

Jacquelynne S. Eccles

Keyword(s):

Theoretical Framework ◽

Expectancy Value ◽

Academic Choices ◽

Motivational Profiles

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Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?

The International Review of Research in Open and Distributed Learning ◽

10.19173/irrodl.v22i1.4978 ◽

2021 ◽

Vol 22 (1) ◽

pp. 41-58

Author(s):

Piret Luik ◽

Marina Lepp

Keyword(s):

Computer Programming ◽

Value Theory ◽

Completion Rate ◽

Dropout Rates ◽

Expectancy Value Theory ◽

Chi Square ◽

Expectancy Value ◽

Chi Square Test ◽

Motivation Scale ◽

Motivational Profiles

Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to compare how these clusters differed in terms of intention to complete and actual completion rate. The sample consisted of 1,181 respondents to the pre-course questionnaire in the Introduction to Programming MOOC. A validated motivation scale, based on expectancy-value theory and k-means cluster analysis, was used to form the groups. The four identified clusters were named as Opportunity motivated (27.7%), Over-motivated (28.6%), Success motivated (19.6%) and Interest motivated (24.0%). Comparison tests and chi-square test were used to describe the differences among the clusters. There were statistically significant differences among clusters in self-evaluated probability of completion. Also, significant differences emerged among three clusters in terms of percentages of respondents who completed the MOOC. Interestingly, the completion rate was the lowest in the Over-motivated cluster. A statistically significant higher ratio of completers to non-completers was found in the Opportunity motivated, Success motivated, and Interest motivated clusters. Our findings can be useful for MOOC instructors, as a better vision of participants’ motivational profiles at the beginning of the MOOC might help to inform the MOOC design to better support different needs, potentially resulting in lower dropout rates.

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The Extended Likeability Framework: A Theoretical Framework for and a Practical Case of Designing Likeable Media Applications for Preschoolers

Advances in Human-Computer Interaction ◽

10.1155/2008/719291 ◽

2008 ◽

Vol 2008 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Vero vanden Abeele ◽

Bieke Zaman

Keyword(s):

Home Environment ◽

Interactive Media ◽

Theoretical Framework ◽

Design Guidelines ◽

Practical Case ◽

Research Project ◽

Expectancy Value ◽

Design Attributes ◽

Ev Model ◽

Media Applications

A theoretical framework and practical case for designing likeable interactive media applications for preschoolers in the home environment are introduced. First, we elaborate on the theoretical framework. We introduce the uses and gratifications paradigm (U&G). We argue that U&G is a good approach to researching likeability of media applications. Next, we complete the U&G framework with expectancy-value (EV) theory. EV theory helps us move from theoretical insights to concrete design guidelines. Together, the U&G framework and the EV model form the foundation of our extended likeability framework for the design and evaluation of interactive media applications, for preschoolers in the home environment. Finally, we demonstrate a practical case of our extended likeability framework via the research project CuTI. The CuTI project aims at revealing those particular user gratifications and design attributes that are important to support playful behaviour and fun activities of preschoolers in the home environment.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Adaptive and Maladaptive Neural Plasticity Due to Facial Nerve Palsy

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000244 ◽

2016 ◽

Vol 224 (2) ◽

pp. 102-111 ◽

Cited By ~ 1

Author(s):

Carsten M. Klingner ◽

Stefan Brodoehl ◽

Gerd F. Volk ◽

Orlando Guntinas-Lichius ◽

Otto W. Witte

Keyword(s):

Facial Nerve ◽

Neural Plasticity ◽

Cortical Plasticity ◽

Brain Plasticity ◽

Motor Nerve ◽

Predictive Coding ◽

Theoretical Framework ◽

Cortical Reorganization ◽

Nerve Lesion ◽

Peripheral Facial Palsy

Abstract. This paper reviews adaptive and maladaptive mechanisms of cortical plasticity in patients suffering from peripheral facial palsy. As the peripheral facial nerve is a pure motor nerve, a facial nerve lesion is causing an exclusive deefferentation without deafferentation. We focus on the question of how the investigation of pure deefferentation adds to our current understanding of brain plasticity which derives from studies on learning and studies on brain lesions. The importance of efference and afference as drivers for cortical plasticity is discussed in addition to the crossmodal influence of different competitive sensory inputs. We make the attempt to integrate the experimental findings of the effects of pure deefferentation within the theoretical framework of cortical responses and predictive coding. We show that the available experimental data can be explained within this theoretical framework which also clarifies the necessity for maladaptive plasticity. Finally, we propose rehabilitation approaches for directing cortical reorganization in the appropriate direction and highlight some challenging questions that are yet unexplored in the field.

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