scholarly journals Learn From Your (Markov) Neighbor: Coenrollment, Assortativity, and Grade Prediction in Undergraduate Courses

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Joshua Patrick Gardner ◽  
Christopher Brooks ◽  
Warren Li
Keyword(s):  
Student Performance ◽  
Large Scale ◽  
Grade Prediction ◽  
University Courses ◽  
Performance Predictors ◽  
Modelling Strategies ◽  
Multiple Network ◽  
Large Scale Networks ◽  
The University ◽  
Law Degree

In this paper, we evaluate the complete undergraduate co-enrollment network over a decade of education at a large American public university. We provide descriptive and exploratory analyses of the network, demonstrating that the co-enrollment networks evaluated follow power-law degree distributions similar to many other large-scale networks; that they reveal strong performance-based assortativity; and that network-based features can improve GPA-based student performance predictors. We model the university-wide undergraduate co-enrollment network as an undirected graph, and implement multiple network-augmented approaches to student grade prediction, including an adaption of the structural modelling approach from (Getoor, 2005,  Lu, 2003}. We compare the performance of this predictor to traditional methods used for grade prediction in undergraduate university courses, and demonstrate that a multi-view ensembling approach outperforms both prior ``flat'' and network-based models for grade prediction across several classification metrics. These findings demonstrate the usefulness of combining diverse approaches in models of student success, and demonstrate specific network-based modelling strategies that are likely to be most effective for grade prediction.

Student Preparation for Dysphagia Practice: Universities and Practicum Sites Supporting a Common Outcome

2016 ◽  
Vol 1 (13) ◽  
pp. 122-129 ◽  
Author(s):  
Wendy Chase ◽  
Lucinda Soares Gonzales
Keyword(s):  
Health Care ◽  
Student Performance ◽  
Classroom Setting ◽  
Medical Settings ◽  
Student Preparation ◽  
Health Care Settings ◽  
The University ◽  
University Of Connecticut

This article will describe the approach to dysphagia education in a classroom setting at the University of Connecticut (UCONN), explore the disparity between student performance in schools vs. health care settings that was discovered at UCONN, and offer suggestions for practicum supervisors in medical settings to enhance student acquisition of competence.

SOCIAL PROJECT AS A PLATFORM FOR THE IMPLEMENTATION OF PROFESSIONAL SKILLS OF FUTURE INFORMATICS TEACHERS

2019 ◽  
pp. 16-22
Author(s):  
E. V. Klimenko ◽  
N. S. Buslova
Keyword(s):  
Large Scale ◽  
Modern Society ◽  
Professional Competencies ◽  
Future Teachers ◽  
Professional Skills ◽  
Knowledge And Skills ◽  
Theoretical Reasoning ◽  
Social Project ◽  
The University

The article is devoted to the consideration of ways to solve one of the actual problems in theory and methodology of training and upbringing — the problem of developing professional skills of future informatics teacher. As a way to adapt students to the profession, the possibility of their involvement in social designing was chosen. Participation in social projects contributes to the approbation and introduction of new forms and methods in teaching informatics. Expanding the experience of future teachers in carrying out large-scale events contributes to the formation of a socially adapted personality competitive in modern society. The potential of a social project in consolidating the knowledge and skills obtained during the theoretical training at the university is indicated. In the article, theoretical reasoning is accompanied by examples of real social projects and activities aimed at the formation of professional competencies of future informatics teachers.

Disruptive innovation and operationalization in local and sustainable food systems: Examining the University of Toronto-Local Food Plus partnership

2019 ◽  
Vol 6 (1) ◽  
pp. 120-139
Author(s):  
Lori Stahlbrand
Keyword(s):  
Food Systems ◽  
Large Scale ◽  
Food System ◽  
Local Food ◽  
Disruptive Innovation ◽  
Government Support ◽  
Sustainable Food ◽  
University Of Toronto ◽  
The University

This paper traces the partnership between the University of Toronto and the non-profit Local Food Plus (LFP) to bring local sustainable food to its St. George campus. At its launch, the partnership represented the largest purchase of local sustainable food at a Canadian university, as well as LFP’s first foray into supporting institutional procurement of local sustainable food. LFP was founded in 2005 with a vision to foster sustainable local food economies. To this end, LFP developed a certification system and a marketing program that matched certified farmers and processors to buyers. LFP emphasized large-scale purchases by public institutions. Using information from in-depth semi-structured key informant interviews, this paper argues that the LFP project was a disruptive innovation that posed a challenge to many dimensions of the established food system. The LFP case study reveals structural obstacles to operationalizing a local and sustainable food system. These include a lack of mid-sized infrastructure serving local farmers, the domination of a rebate system of purchasing controlled by an oligopolistic foodservice sector, and embedded government support of export agriculture. This case study is an example of praxis, as the author was the founder of LFP, as well as an academic researcher and analyst.

scholarly journals Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

2021 ◽  
Author(s):  
Miguel Dasilva ◽  
Christian Brandt ◽  
Marc Alwin Gieselmann ◽  
Claudia Distler ◽  
Alexander Thiele
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Receptor Activation ◽  
Frontal Eye Field ◽  
Control Signals ◽  
Cognitive Operations ◽  
Eye Field ◽  
Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

scholarly journals Effects of Socio-Academic Intervention on Student Performance in Vulnerable Groups

2021 ◽  
Vol 13 (14) ◽  
pp. 7673
Author(s):  
Tarquino Sánchez-Almeida ◽  
David Naranjo ◽  
Raquel Gilar-Corbi ◽  
Jessica Reina
Keyword(s):  
Higher Education ◽  
Academic Performance ◽  
Student Performance ◽  
Academic Intervention ◽  
Vulnerable Groups ◽  
University System ◽  
Higher Education System ◽  
Economic Support ◽  
Support Mechanisms ◽  
The University

In Ecuador, affirmative action policies enable students from vulnerable groups to preferentially enter universities. However, these policies are limited to admission and do not include academic or socio-economic support mechanisms that, according to the literature, promote student insertion in the higher education system. In this study, the effects of socio-academic intervention on the academic performance of vulnerable students are presented. For this, 41 students were selected among 164 vulnerable students entering the Escuela Politécnica Nacional in the second term of 2019. The 41 students attended a socio-academic intervention course for one term, while the remaining 123 attended the Escuela Politécnica Nacional levelling course directly. Once both groups of students finished the levelling course, their performance in each of the course subjects was compared. The results showed that the academic performance of the students in the intervention was significantly higher in mathematics and geometry compared to the students who had no intervention. These results show that the socio-academic intervention promotes the real insertion of vulnerable students in the university system.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

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