scholarly journals A Proposal for Performance-based Assessment of the Learning of Machine Learning Concepts and Practices in K-12

Author(s):  
Christiane GRESSE VON WANGENHEIM ◽  
Nathalia da Cruz ALVES ◽  
Marcelo F. RAUBER ◽  
Jean C. R. HAUCK ◽  
Ibrahim H. YETER
2017 ◽  
Vol 13 (8) ◽  
pp. 1584-1596 ◽  
Author(s):  
Sutanu Nandi ◽  
Abhishek Subramanian ◽  
Ram Rup Sarkar

We propose an integrated machine learning process to predict gene essentiality in Escherichia coli K-12 MG1655 metabolism that outperforms known methods.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Matti Tedre ◽  
Tapani Toivonen ◽  
Henriikka Vartiainen ◽  
Ilkka Jormanainen ◽  
Teemu Valtonen ◽  
...  

2021 ◽  
Author(s):  
Ismaila Temitayo Sanusi ◽  
Solomon Sunday Oyelere ◽  
Friday Joseph Agbo ◽  
Jarkko Suhonen
Keyword(s):  

Author(s):  
Christiane Gresse von Wangenheim ◽  
Jean C. R. Hauck ◽  
Fernando S. Pacheco ◽  
Matheus F. Bertonceli Bueno

Author(s):  
Matti Tedre ◽  
Henriikka Vartiainen ◽  
Juho Kahila ◽  
Tapani Toivonen ◽  
Ilkka Jormanainen ◽  
...  

Author(s):  
Shubo Chen ◽  
Binsen Qian ◽  
Harry Cheng

In this paper, we provide a new voice recognition framework which allows K-12 students to write programs to solve problems using voice control. The framework contains the voice recognition module SPHINX which is based on an open source machine learning tool developed by Carnegie Mellon University and a wrapper function which is written in C/C++ interpreter Ch. The wrapper function allows students to interact the module in Ch. Along with Ch programming and robotic coursework, students will get the chance to learn the basic concept of machine learning and voice recognition technique. In order to bring students attention and interest in machine learning, various tasks have been designed for students to accomplish based on the framework. The framework is also flexible for them to explore other interesting projects.


2020 ◽  
Vol 48 (4) ◽  
pp. 199-212 ◽  
Author(s):  
Jui-Long Hung ◽  
Kerry Rice ◽  
Jennifer Kepka ◽  
Juan Yang

Purpose For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis. Design/methodology/approach This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach. Findings The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students. Originality/value The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.


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