What Do Local Government Education Managers Do to Boost Learning Outcomes?

2021 ◽  
Author(s):  
Jacobus Cilliers ◽  
Eric Dunford ◽  
James Habyarimana
Keyword(s):  
Local Government ◽  
Learning Outcomes ◽  
Management Practices ◽  
Machine Learning Techniques ◽  
Teacher Incentives ◽  
Performance Review ◽  
Comprehensive Management ◽  
Learning Techniques ◽  
Frequent Monitoring ◽  
Government Education

Decentralization reforms have shifted responsibility for public service delivery to local government, yet little is known about how their management practices or behavior shape performance. We conducted a comprehensive management survey of mid-level education bureaucrats and their staff in every district in Tanzania, and employ flexible machine learning techniques to identify important management practices associated with learning outcomes. We find that management practices explain 10 percent of variation in a district's exam performance. The three management practices most predictive of performance are: i) the frequency of school visits; ii) school and teacher incentives administered by the district manager; and iii) performance review of staff. Although the model is not causal, these findings suggest the importance of robust systems to motivate district staff, schools, and teachers, that include frequent monitoring of schools. They also show the importance of surveying subordinates of managers, in order to produce richer information on management practices.

scholarly journals Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5340
Author(s):  
Haocheng Xu ◽  
Shenghong Li ◽  
Caroline Lee ◽  
Wei Ni ◽  
David Abbott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Interactions ◽  
Management Practices ◽  
Clustering Algorithms ◽  
Physical Distance ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Video Recordings ◽  
Learning Techniques ◽  
The Social

Understanding social interactions in livestock groups could improve management practices, but this can be difficult and time-consuming using traditional methods of live observations and video recordings. Sensor technologies and machine learning techniques could provide insight not previously possible. In this study, based on the animals’ location information acquired by a new cooperative wireless localisation system, unsupervised machine learning approaches were performed to identify the social structure of a small group of cattle yearlings (n=10) and the social behaviour of an individual. The paper first defined the affinity between an animal pair based on the ranks of their distance. Unsupervised clustering algorithms were then performed, including K-means clustering and agglomerative hierarchical clustering. In particular, K-means clustering was applied based on logical and physical distance. By comparing the clustering result based on logical distance and physical distance, the leader animals and the influence of an individual in a herd of cattle were identified, which provides valuable information for studying the behaviour of animal herds. Improvements in device robustness and replication of this work would confirm the practical application of this technology and analysis methodologies.

scholarly journals Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

2021 ◽  
Vol 13 (4) ◽  
pp. 2025 ◽  
Author(s):  
Fotis Kitsios ◽  
Maria Kamariotou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Model ◽  
Business Strategy ◽  
Management Practices ◽  
Corporate Strategy ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Practical Implementation ◽  
Learning Techniques

In the past decade, current literature and businesses have drawn attention to Artificial Intelligence (AI) tools and in particular to the advances in machine learning techniques. Nevertheless, while the AI technology offers great potential to solve difficulties, challenges remain implicated in practical implementation and lack of expertise in the strategic usage of AI to create business value. This paper aims to implement a systematic literature review analyzing convergence of the AI and corporate strategy and develop a theoretical model incorporating issues based on the existing research in this field. Eighty-one peer-reviewed articles were discussed on the basis of research methodology from Webster and Watson (2002). In addition to gaps in future research, a theoretical model is developed, discussing the four sources of value creation: AI and Machine Learning in organizations; alignment of AI tools and Information Technology (IT) with organizational strategy; AI, knowledge management and decision-making process; and AI, service innovation and value. These outcomes lead to both theoretical and managerial viewpoints, with extensive possibilities to generate new methods and types of management practices.

scholarly journals Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2620
Author(s):  
María Consuelo Sáiz-Manzanares ◽  
Raúl Marticorena-Sánchez ◽  
Javier Ochoa-Orihuel
Keyword(s):  
Machine Learning ◽  
Student Satisfaction ◽  
Learning Outcomes ◽  
Educational Data Mining ◽  
Learning Technologies ◽  
Machine Learning Techniques ◽  
Self Regulated Learning ◽  
Learning Techniques ◽  
Automatic Feedback ◽  
Teaching Learning

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.

Identification of prediabetes discussions in unstructured clinical documentation using natural language processing methods (Preprint)

2021 ◽  
Author(s):  
Jessica Schwartz ◽  
Eva Tseng ◽  
Nisa M Maruthur ◽  
Masoud Rouhizadeh
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Human Performance ◽  
Management Practices ◽  
Search Strategy ◽  
Keyword Search ◽  
Machine Learning Techniques ◽  
Clinical Documentation ◽  
Machine Learning Classifiers ◽  
Learning Techniques

BACKGROUND Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions so understanding how providers discuss prediabetes with patients will inform how to improve their care. OBJECTIVE Develop an NLP algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation. METHODS We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied seven machine learning models against our manual annotation. RESULTS Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation. CONCLUSIONS We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.

scholarly journals Instructor-learner brain coupling discriminates between instructional approaches and predicts learning

2019 ◽  
Author(s):  
Yafeng Pan ◽  
Suzanne Dikker ◽  
Pavel Goldstein ◽  
Yi Zhu ◽  
Cuirong Yang ◽  
...  
Keyword(s):  
Learning Outcomes ◽  
Near Infrared ◽  
Brain Activity ◽  
Neural Mechanisms ◽  
Machine Learning Techniques ◽  
Instructional Approaches ◽  
Functional Near Infrared Spectroscopy ◽  
Instructional Process ◽  
Learning Techniques ◽  
Constructive Engagement

AbstractThe neural mechanisms that support naturalistic learning via effective pedagogical approaches remain elusive. Here we use functional near-infrared spectroscopy to measure brain activity from instructor-learner dyads simultaneously during dynamic conceptual learning. We report that brain-to-brain coupling is correlated with learning outcomes, and, crucially, appears to be driven by specific scaffolding behaviors on the part of the instructors (e.g., asking guiding questions or providing hints). Brain-to-brain coupling enhancement is absent when instructors use an explanation approach (e.g., providing definitions or clarifications). Finally, we find that machine-learning techniques are more successful when decoding instructional approaches (scaffolding vs. explanation) from brain-to-brain coupling data than when using a single-brain method. These findings suggest that brain-to-brain coupling as a pedagogically relevant measure tracks the naturalistic instructional process during instructor-learner interaction throughout constructive engagement, but not information clarification.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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