A Technique for Inspiring Scientific Inquiry Using a Creative Scenario

Teaching scientific inquiry in large interdisciplinary classes is a challenge. We describe a creative problem-based learning approach, using a motivational island crisis scenario, to inspire research design. Students were empowered to formulate their individual scientific inquiry and then guided to develop a testable hypothesis, aims, and objectives in designing a research proposal. Personalized data sets matched to the research objectives were provided to individual students for analysis and presentation. This technique helps students to gain critical insights into the global value of interdisciplinary collaboration toward solving complex real-world problems. Students learn the front end of research, how to formulate a line of scientific inquiry and design an innovative research project—both important skills for them as tomorrow's leaders and entrepreneurs.

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Beyond problem-based learning: using dynamic PBL in chemistry

Chemistry Education Research and Practice ◽

10.1039/c4rp00248b ◽

2015 ◽

Vol 16 (2) ◽

pp. 251-259 ◽

Cited By ~ 26

Author(s):

Tina L. Overton ◽

Christopher A. Randles

Keyword(s):

Sustainable Development ◽

Real World ◽

Dynamic Problem ◽

Problem Based Learning ◽

Learning Activity ◽

First Year ◽

Learning Approach ◽

Data Sets ◽

Transferable Skills ◽

Real World Problems

This paper describes the development and implementation of a novel pedagogy, dynamic problem-based learning. The pedagogy utilises real-world problems that evolve throughout the problem-based learning activity and provide students with choice and different data sets. This new dynamic problem-based learning approach was utilised to teach sustainable development to first year chemistry undergraduates. Results indicate that the resources described here motivated students to learn about sustainability and successfully developed a range of transferable skills.

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TESTING TIME-SERIES STATIONARITY AGAINST AN ALTERNATIVE WHOSE MEAN IS PERIODIC

Macroeconomic Dynamics ◽

10.1017/s1365100500020034 ◽

2001 ◽

Vol 5 (3) ◽

pp. 380-412 ◽

Cited By ~ 10

Author(s):

Melvin A. Hinich ◽

Phillip Wild

Keyword(s):

Time Series ◽

Fourier Transform ◽

Real World ◽

Null Hypothesis ◽

Data Sets ◽

Testing Time ◽

Economic Data ◽

The Mean ◽

Important Form ◽

Real World Problems

We develop a test of the null hypothesis that an observed time series is a realization of a strictly stationary random process. Our test is based on the result that the kth value of the discrete Fourier transform of a sample frame has a zero mean under the null hypothesis. The test that we develop will have considerable power against an important form of nonstationarity hitherto not considered in the mainstream econometric time-series literature, that is, where the mean of a time series is periodic with random variation in its periodic structure. The size and power properties of the test are investigated and its applicability to real-world problems is demonstrated by application to three economic data sets.

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Multi-View Multi-Label Learning with View-Specific Information Extraction

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/539 ◽

2019 ◽

Cited By ~ 4

Author(s):

Xuan Wu ◽

Qing-Guo Chen ◽

Yao Hu ◽

Dengbao Wang ◽

Xiaodong Chang ◽

...

Keyword(s):

Information Extraction ◽

Real World ◽

State Of The Art ◽

Specific Information ◽

Learning Approach ◽

Data Sets ◽

Learning Approaches ◽

Real World Data ◽

Learning Techniques ◽

Shared Information

Multi-view multi-label learning serves an important framework to learn from objects with diverse representations and rich semantics. Existing multi-view multi-label learning techniques focus on exploiting shared subspace for fusing multi-view representations, where helpful view-specific information for discriminative modeling is usually ignored. In this paper, a novel multi-view multi-label learning approach named SIMM is proposed which leverages shared subspace exploitation and view-specific information extraction. For shared subspace exploitation, SIMM jointly minimizes confusion adversarial loss and multi-label loss to utilize shared information from all views. For view-specific information extraction, SIMM enforces an orthogonal constraint w.r.t. the shared subspace to utilize view-specific discriminative information. Extensive experiments on real-world data sets clearly show the favorable performance of SIMM against other state-of-the-art multi-view multi-label learning approaches.

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Using the Creative Problem Solving Profile (CPSP) for Diagnosing and Solving Real-World Problems

Emergence ◽

10.1207/s15327000em0503_5 ◽

2003 ◽

Vol 5 (3) ◽

pp. 22-47 ◽

Cited By ~ 6

Author(s):

Min Basadur ◽

Garry Gelade

Keyword(s):

Problem Solving ◽

Real World ◽

Creative Problem Solving ◽

Creative Problem ◽

Real World Problems

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Self-supervised Regularization for Text Classification

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00389 ◽

2021 ◽

Vol 9 ◽

pp. 641-656

Author(s):

Meng Zhou ◽

Zechen Li ◽

Pengtao Xie

Keyword(s):

Text Classification ◽

Real World ◽

Input Data ◽

Supervised Classification ◽

Classification Task ◽

Learning Approach ◽

Classification Models ◽

Data Representations ◽

Class Labels ◽

Real World Problems

Abstract Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose SSL-Reg, a data-dependent regularization approach based on self-supervised learning (SSL). SSL (Devlin et al., 2019a) is an unsupervised learning approach that defines auxiliary tasks on input data without using any human-provided labels and learns data representations by solving these auxiliary tasks. In SSL-Reg, a supervised classification task and an unsupervised SSL task are performed simultaneously. The SSL task is unsupervised, which is defined purely on input texts without using any human- provided labels. Training a model using an SSL task can prevent the model from being overfitted to a limited number of class labels in the classification task. Experiments on 17 text classification datasets demonstrate the effectiveness of our proposed method. Code is available at https://github.com/UCSD-AI4H/SSReg.

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Reformulating the Statics Course: A Design-Based Approach

Volume 9: Engineering Education and Professional Development ◽

10.1115/imece2008-69196 ◽

2008 ◽

Author(s):

Sridhar S. Condoor

Keyword(s):

Real World ◽

Design Theory ◽

Building Blocks ◽

Learning Approach ◽

Course Content ◽

Systematic Improvement ◽

Implementation Scheme ◽

Systemic Problems ◽

Real World Problems

Statics is a pivotal course, whose concepts serve as the building blocks for future courses in engineering. From the experience of teaching the follow-on courses to statics, we found several systemic problems present in most statics textbooks. These problems manifest themselves as lower-than-expected abilities in the students when applying the concepts to design/analyze real systems in subsequent courses. The resulting disappointment in engineering educators is common and well documented. The pedagogy outlined in this paper is based on the premise that students learn more effectively when the relevance of the concepts to real world problems and a systematic improvement in their skill set is tactilely, emotionally, and rationally understood. To this end, the pedagogy brings design theory into the course content. The paper discusses rationale behind the pedagogy and its possible implementation scheme with examples. The pedagogy is flexible and can be integrated into an existing learning approach.

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