scholarly journals Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

2015 ◽  
Vol 12 (7) ◽  
pp. 6327-6350
Author(s):  
C. A. Sanchez ◽  
B. L. Ruddell ◽  
R. Schiesser ◽  
V. Merwade
Keyword(s):  
Positive Impact ◽  
Data Driven ◽  
Student Perspective ◽  
Use Of Data ◽  
Professional Perspective ◽  
Simulation Group ◽  
Lower Division ◽  
Job Duties ◽  
Using Data ◽  
Data Driven Modeling

Abstract. Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

scholarly journals Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

2016 ◽  
Vol 20 (3) ◽  
pp. 1289-1299 ◽  
Author(s):  
Christopher A. Sanchez ◽  
Benjamin L. Ruddell ◽  
Roy Schiesser ◽  
Venkatesh Merwade
Keyword(s):  
Positive Impact ◽  
Data Driven ◽  
Student Perspective ◽  
Use Of Data ◽  
Professional Perspective ◽  
Simulation Group ◽  
Lower Division ◽  
Job Duties ◽  
Using Data ◽  
Data Driven Modeling

Abstract. Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data-driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower-division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

scholarly journals Analysis of Decision Making Process in Moneyball: The Art of Winning an Unfair Game

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 57
Author(s):  
Mochamad Sandy Triady ◽  
Ami Fitri Utami
Keyword(s):  
Decision Making ◽  
Excellent Result ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
General Manager ◽  
Use Of Data ◽  
Bottom Position ◽  
Using Data ◽  
Baseball Team

Billy Beanes’s success in using data-driven decision making in baseball industry is wonderfully written by Michael Lewis in Moneyball. As a general manager in baseball team that were in the bottom position of the league from the financial side to acquire the players, Beane, along with his partner, explored the use of data in choosing the team’s player. They figured out how to determine the worth of every player.The process was not smooth, due to the condition of baseball industry that was not common with using advanced statistic in acquiring   players. Many teams still use the old paradigm that rely on experts’ judgments, intuition, or experience in decision making process. Moneyball approached that using data-driven decision making gave excellent result for Beane’s team. The team won 20 gamessequently in the 2002 season and also spent the lowest cost per win than other teams.This paper attempts to review the principles of Moneyball – The Art of Winning an Unfair Game as a process of decision making and gives what we can learn from the story in order to win the games, the unfair games.

scholarly journals Data-driven decision-making in creating class rosters

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rebecca Wolf ◽  
Joseph M. Reilly ◽  
Steven M. Ross
Keyword(s):  
Decision Making ◽  
Student Learning ◽  
Low Income ◽  
School Leaders ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Use Of Data ◽  
Using Data ◽  
Rich Data

PurposeThis article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.Design/methodology/approachA literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.FindingsFindings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.Practical implicationsGiven the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.Originality/valueThis article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.

scholarly journals A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling

2018 ◽  
Vol 3 (2) ◽  
pp. 245-263
Author(s):  
Franco van Wyk ◽  
Anahita Khojandi ◽  
Brian Williams ◽  
Don MacMillan ◽  
Robert L. Davis ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Data Driven ◽  
Physiological Data ◽  
Benefit Analysis ◽  
Data Acquisition Systems ◽  
Using Data ◽  
Data Driven Modeling

scholarly journals On the Data-Driven Modeling of Reactive Extrusion

Fluids ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 94 ◽  
Author(s):  
Ruben Ibañez ◽  
Fanny Casteran ◽  
Clara Argerich ◽  
Chady Ghnatios ◽  
Nicolas Hascoet ◽  
...  
Keyword(s):  
Reactive Extrusion ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Reactive Polymer ◽  
Complex Processes ◽  
Use Of Data ◽  
Learning Techniques ◽  
Twin Screw ◽  
Data Driven Modeling

This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.

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