Applying Graph Theory to Examine the Dynamics of Student Discussions in Small-Group Learning.
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ABSTRACT:
SUBMITTER: Chai A
PROVIDER: S-EPMC6755212 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationTo understand protein structure, folding and function fully and to design proteins de novo reliably, we must learn from natural protein structures that have been characterized experimentally. The number of protein structures available is large and growing exponentially, which makes this task challenging. Indeed, computational resources are becoming increasingly important for classifying and analyzing this resource. Here, we use tools from graph theory to define an Atlas classification scheme for automatically categorizing certain protein substructures.ResultsFocusing on the α-helical coiled coils, which are ubiquitous protein-structure and protein-protein interaction motifs, we present a suite of computational resources designed for analyzing these assemblies. iSOCKET enables interactive analysis of side-chain packing within proteins to identify coiled coils automatically and with considerable user control. Applying a graph theory-based Atlas classification scheme to structures identified by iSOCKET gives the Atlas of Coiled Coils, a fully automated, updated overview of extant coiled coils. The utility of this approach is illustrated with the first formal classification of an emerging subclass of coiled coils called α-helical barrels. Furthermore, in the Atlas, the known coiled-coil universe is presented alongside a partial enumeration of the 'dark matter' of coiled-coil structures; i.e. those coiled-coil architectures that are theoretically possible but have not been observed to date, and thus present defined targets for protein design.Availability and implementationiSOCKET is available as part of the open-source GitHub repository associated with this work (https://github.com/woolfson-group/isocket). This repository also contains all the data generated when classifying the protein graphs. The Atlas of Coiled Coils is available at: http://coiledcoils.chm.bris.ac.uk/atlas/app.
| S-EPMC6157074 | biostudies-literature
Project description:ObjectiveAdherence to adjuvant endocrine therapy (AET) in breast cancer survivors is suboptimal. Using the theory of planned behavior (TPB), this study aimed to identify the strongest predictors from the TPB of AET intentions and past behavior and assessed whether ambivalence and anticipatory emotions increased the predictive capacity of TPB.MethodsTwo hundred eighty women diagnosed with hormone positive (HR+) breast cancer who filled at least one prescription of AET responded to a survey measuring TPB constructs, attitudinal ambivalence, and anticipatory emotions. The outcomes were intentions to adhere to AET and past medication adherence (previous 2 weeks).ResultsThe TPB explained 66% of intentions to adhere to AET (P < 0.001). Ambivalence did not improve the TPB model's predictive value. When emotions were included with TPB, the model explained 70% of adherence intentions F11,226 = 52.84, P < 0.001 (R2c = .70). This increase of 4% in predictability was statistically significant (ΔR2 = 0.04), F6, 226 = 7.90, P < 0.001. Women who self-reported nonadherence in the past 2 weeks differed significantly in the TPB variables, ambivalence, and anticipatory emotions from adherent women. Nonadherent participants reported lower-future intentions to adhere F1, 236 = 5.63, P = 0.018.ConclusionsResults suggest key concepts, such as anticipatory positive emotions that should be addressed in future interventions to enhance AET adherence and survivorship.
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Project description:BackgroundActive learning relies on students' engagement with teachers, study materials and/or each other. Although medical education has adopted active learning as a core component of medical training, teachers have difficulties recognising when and why their students engage or disengage and how to teach in ways that optimise engagement. With a better understanding of the dynamics of student engagement in small-group active learning settings, teachers could be facilitated in effectively engaging their students.MethodsWe conducted a video-stimulated recall study to explore medical students' engagement during small-group learning activities. We recorded one teaching session of two different groups and selected critical moments of apparent (dis)engagement. These moments served as prompts for the 15 individual semi-structured interviews we held. Interview data were analysed using Template Analysis style of thematic analysis. To guide the analysis, we used a framework that describes student engagement as a dynamic and multidimensional concept, consisting of behavioural, cognitive and emotional components.ResultsThe analysis uncovered three main findings: (1) In-class student engagement followed a spiral-like pattern. Once students were engaged or disengaged on one dimension, other dimensions were likely to follow suit. (2) Students' willingness to engage in class was decided before class, depending on their perception of a number of personal, social and educational antecedents of engagement. (3) Distinguishing engagement from disengagement appeared to be difficult for teachers, because the intention behind student behaviour was not always identifiable.DiscussionThis study adds to the literature by illuminating the dynamic process of student engagement and explaining the difficulty of recognising and influencing this process in practice. Based on the importance of discerning the intentions behind student behaviour, we advise teachers to use their observations of student (dis)engagement to initiate interaction with students with open and inviting prompts. This can help teachers to (re-)engage students in their classrooms.
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Project description:In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.
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Project description:Past research on group work has primarily focused on promoting change through implementation of interventions designed to increase performance. Recently, however, education researchers have called for more descriptive analyses of group interactions. Through detailed qualitative analysis of recorded discussions, we studied the natural interactions of students during group work in the context of a biology laboratory course. We analyzed multiple interactions of 30 different groups as well as data from each of the 91 individual participants to characterize the ways students engage in discussion and how group dynamics promote or prevent meaningful discussion. Using a set of codes describing 15 unique behaviors, we determined that the most common behavior seen in student dialogue was analyzing data, followed by recalling information and repeating ideas. We also classified students into one of 10 different roles for each discussion, determined by their most common behaviors. We found that, although students cooperated with one another by exchanging information, they less frequently fully collaborated to explain their conclusions through the exchange of reasoning. Within this context, these findings show that students working in groups generally choose specific roles during discussions and focus on data analysis rather than constructing logical reasoning chains to explain their conclusions.
| S-EPMC8693937 | biostudies-literature
Project description:Prior studies have reported inconsistent findings with regard to the effects of small-group student talk on developing individual students' English-as-a-foreign-language (EFL) writing ability. To further explore the question under discussion, we designed a quasi-experimental study that included a pretest, a posttest, and a delayed posttest, and implemented it in two English-major groups at a university in China. We randomly assigned the students to an intervention group and a comparison group to investigate whether employing structured small-group student talk as collaborative prewriting discussions would effectively facilitate individual students' EFL writing development and whether such effects could be retained. The immediate and sustained effects after the quasi-experimental study was completed were measured by the analytic scores on five components of the writing task (content, organization, vocabulary, language, and mechanics) and the holistic writing scores cumulated of all these components. Statistical analyses revealed that the two groups were significantly distinguished by their analytic and holistic scores, indicating that students in the intervention group outperformed their comparison group peers in writing performance. The effects of collaborative prewriting discussions in the form of structured small-group student talk were found statistically significant in facilitating students' writing improvement in the content, organization, vocabulary, and language use, but not mechanics. The effects on content, organization, and vocabulary were retained as seen from the delayed posttest, while those on language use were not. The comparison group showed little improvement in their writing performance across the three tests. We concluded this study with a discussion on the implications for English-as-a-second/foreign-language (L2) writing instruction.
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Project description:Collectives adapt their network structure to the challenges they face. It has been hypothesized that collectives experiencing a real or imagined threat from an outgroup tend to consolidate behind a few group members, and that network structures in which a few members attract most of the attention are more likely in politically extreme groups. These hypotheses have not been tested in large-scale real-world settings. We reconstruct networks of tens of thousands of commenters participating in comment sections of high-profile U.S. political news websites spanning the political spectrum from left to right, including Mother Jones, The Atlantic, The Hill, and Breitbart. We investigate the relationship between different indices of inequality of attention in commenters' networks and perceived group threat associated with significant societal events, from elections and political rallies to mass shootings. Our findings support the hypotheses that groups facing a real or imagined outgroup threat and groups that are more politically extreme are more likely to attend to a few high-profile members. These results provide an extensive real-world test of theoretical accounts of collective adaptation to outgroup threats.
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Project description:BACKGROUND:Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects. RESULTS:This work integrates two approaches--machine learning algorithms and topological parameter-based classification--to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements. CONCLUSIONS:With the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs.
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Project description:Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.
| S-EPMC5871915 | biostudies-literature
Project description:BackgroundIn order to best prepare medical students for their increasingly complex future career, interdisciplinary higher education is swiftly gaining popularity. However, the implementation of interdisciplinary learning in medical education is challenging. The present study deepens the understanding of the challenges and opportunities inherent to the implementation of an interdisciplinary course. We elucidated the attitudes and beliefs of students participating in a newly developed interdisciplinary minor, in which students of medicine (MS) and communication and information sciences (CISS) were involved.MethodsWe conducted four semi-structured focus group interviews, of which two were held before, and two were held after the course. Seven MS and six CISS participated voluntarily. A pre-arranged interview guide was used. The interviews were recorded and afterwards systematically analyzed with the 'constant comparative analysis' technique.ResultsThe focus group interviews revealed three differences in epistemics between students in terms of 1) curriculum content, 2) educational formats and 3) student's competence perceptions. These factors influenced the way students evaluated themselves, each other and the interdisciplinary course.ConclusionsWe conclude that factors that influence interdisciplinary learning are personal epistemics, individual learning preferences, and the synergy that is achieved throughout interdisciplinary learning. Organizing the dialogue among students of different disciplines could make students aware of inequalities, implicated biases and assigned status of different student groups. These empirical results are crucial to tailor interdisciplinary education to each specific discipline and to take interdisciplinary learning to a higher level of maturity.
| S-EPMC10024401 | biostudies-literature