The Use of a Representational Triplet Model as the Basis for the Evaluation of Students’ Representational Competence

2018 ◽  
pp. 247-262
Author(s):  
Jill D. Maroo ◽  
Sara L. Johnson
Keyword(s):  
Representational Competence ◽  
Triplet Model

Chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills

2021 ◽  
Author(s):  
Maia Popova ◽  
Tamera Jones
Keyword(s):  
Professional Development ◽  
Coding Theory ◽  
Assessment Practices ◽  
Self Awareness ◽  
Scientific Practices ◽  
Information Processing Model ◽  
Representational Competence ◽  
Evidence Based Practices ◽  
Factors Affecting ◽  
The Us

Representational competence is one's ability to use disciplinary representations for learning, communicating, and problem-solving. These skills are at the heart of engagement in scientific practices and were recognized by the ACS Examinations Institute as one of ten anchoring concepts. Despite the important role that representational competence plays in student success in chemistry and the considerable number of investigations into students’ ability to reason with representations, very few studies have examined chemistry instructors’ approaches toward developing student representational competence. This study interviewed thirteen chemistry instructors from eleven different universities across the US about their intentions to develop, teach, and assess student representational competence skills. We found that most instructors do not aim to help students develop any representational competence skills. At the same time, participants’ descriptions of their instructional and assessment practices revealed that, without realizing it, most are likely to teach and assess several representational competence skills in their courses. A closer examination of these skills revealed a focus on lower-level representational competence skills (e.g., the ability to interpret and generate representations) and a lack of a focus on higher-level meta-representational competence skills (e.g., the ability to describe affordances and limitations of representations). Finally, some instructors reported self-awareness about their lack of knowledge about effective teaching about representations and the majority expressed a desire for professional development opportunities to learn about differences in how experts and novices conceptualize representations, about evidence-based practices for teaching about representations, and about how to assess student mastery of representational competence skills. This study holds clear implications for informing chemistry instructors’ professional development initiatives. Such training needs to help instructors take cognizance of relevant theories of learning (e.g., constructivism, dual-coding theory, information processing model, Johnstone's triangle), and the key factors affecting students’ ability to reason with representations, as well as foster awareness of representational competence skills and how to support students in learning with representations.

scholarly journals Thermally Enhanced Cable Insulation for the Nb-Ti High Luminosity LHC Inner Triplet Model

2012 ◽  
Vol 22 (3) ◽  
pp. 7700404-7700404 ◽  
Author(s):  
P. P. Granieri ◽  
P. Fessia ◽  
D. Richter ◽  
D. Tommasini
Keyword(s):  
High Luminosity ◽  
Cable Insulation ◽  
Triplet Model

scholarly journals A modular invariant bulk theory for the \boldsymbol{c=0} triplet model

2010 ◽  
Vol 44 (1) ◽  
pp. 015204 ◽  
Author(s):  
Matthias R Gaberdiel ◽  
Ingo Runkel ◽  
Simon Wood
Keyword(s):  
Modular Invariant ◽  
Triplet Model

scholarly journals Learning with Friction—Students’ Gestures and Enactment in Relation to a GeoGebra Simulation

2021 ◽  
Author(s):  
Lorena Solvang ◽  
Jesper Haglund
Keyword(s):  
Learning Environment ◽  
Video Data ◽  
Digital Learning ◽  
School Students ◽  
Representational Competence ◽  
Physics Students ◽  
Digital Learning Environment ◽  
Upper Secondary ◽  
Collaborative Learning Environment ◽  
Why Questions

AbstractThe present study contributes to the understanding of physics students’ representational competence by examining specific bodily practices (e.g. gestures, enactment) of students’ interaction and constructions of representations in relation to a digital learning environment. We present and analyse video data of upper-secondary school students’ interaction with a GeoGebra simulation of friction. Our analysis is based on the assumption that, in a collaborative learning environment, students use their bodies as means of dealing with interpretational problems, and that exploring students’ gestures and enactment can be used to analyse their sensemaking processes. This study shows that specific features of the simulation—features connected with microscopic aspects of friction—triggered students to ask what-if and why questions and consequently, to learn about the representation. During this sense-making process, students improvised their own representations to make their ideas more explicit. The findings extend current research on students’ representational competence by bringing attention to the role of students’ generation of improvised representations in the processes of learning with and about representations.

Remarks on the Possible Existence of Stable or Semistable Charmed Particles in the Three-Triplet Model

1971 ◽  
Vol 3 (5) ◽  
pp. 1173-1177 ◽  
Author(s):  
J. C. Pati ◽  
C. H. Woo
Keyword(s):  
Triplet Model

scholarly journals and the triplet model

2010 ◽  
Vol 835 (3) ◽  
pp. 314-342 ◽  
Author(s):  
David Ridout
Keyword(s):  
Triplet Model

scholarly journals Autonomous emergence of connectivity assemblies via spike triplet interactions

2019 ◽  
Author(s):  
Lisandro Montangie ◽  
Julijana Gjorgjieva
Keyword(s):  
Random Network ◽  
Transmission Function ◽  
Higher Order ◽  
External Input ◽  
Spike Timing ◽  
Symmetric Pair ◽  
Graph Theoretic ◽  
Stdp Rule ◽  
Triplet Model ◽  
External Stimuli

AbstractNon-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP) which depends on the interactions of three precisely-timed spikes and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We describe synaptic changes arising from emergent higher-order correlations, and investigate their influence on different connectivity motifs in the network. Our motif expansion framework reveals novel motif structures under the triplet STDP rule, which support the formation of bidirectional connections and loops in contrast to the classical pair-based STDP rule. Therefore, triplet STDP drives the spontaneous emergence of self-connected groups of neurons, or assemblies, proposed to represent functional units in neural circuits. Assembly formation has often been associated with plasticity driven by firing rates or external stimuli. We propose that assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule commonly assumed in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of triplet STDP or the synaptic transmission function differentially promote connectivity motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures.

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