Using machine learning to analyze and predict construction task productivity

2022 ◽  
Author(s):  
Laura Florez‐Perez ◽  
Zhiyuan Song ◽  
Jean C. Cortissoz
Keyword(s):  
Machine Learning ◽  
Construction Task

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

Manipulate Me

2020 ◽  
Vol 36 (4) ◽  
pp. 554-562
Author(s):  
Alica Thissen ◽  
Frank M. Spinath ◽  
Nicolas Becker
Keyword(s):  
High School Students ◽  
Spatial Ability ◽  
Convergent Validity ◽  
Item Difficulty ◽  
Test Results ◽  
Response Elimination ◽  
School Students ◽  
Test Environment ◽  
Response Options ◽  
Construction Task

Abstract. The cube construction task represents a novel format in the assessment of spatial ability through mental cube rotation tasks. Instead of selecting the correct answer from several response options, respondents construct their own response in a computerized test environment, leading to a higher demand for spatial ability. In the present study with a sample of 146 German high-school students, we tested an approach to manipulate the item difficulties in order to create items with a greater difficulty range. Furthermore, we compared the cube task in a distractor-free and a distractor-based version while the item stems were held identical. The average item difficulty of the distractor-free format was significantly higher than in the distractor-based format ( M = 0.27 vs. M = 0.46) and the distractor-free format showed a broader range of item difficulties (.02 ≤  pi ≤ .95 vs. .37 ≤  pi ≤ .63). The analyses of the test results also showed that the distractor-free format had a significantly higher correlation with a broad intelligence test ( r = .57 vs. r = .17). Reasons for the higher convergent validity of the distractor-free format (prevention of response elimination strategies and the broader range of item difficulties) and further research possibilities are discussed.

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
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