Development of a Basic Learning Ability Contents Scale for Adaptation to Majors Strengthening Learning Capabilities

2020 ◽  
Vol 20 (4) ◽  
pp. 65-85
Author(s):  
Seung Hee Yang ◽  
Myeung Hee Lim
Keyword(s):  
Learning Ability ◽  
Learning Capabilities ◽  
Basic Learning

scholarly journals Student Classification Based on Cognitive Abilities and Predicting Learning Performances using Machine Learning Models

2020 ◽  
Vol 8 (6) ◽  
pp. 3554-3569
Keyword(s):  
Machine Learning ◽  
Cognitive Abilities ◽  
Engineering Students ◽  
Demographic Data ◽  
Learning Preferences ◽  
Learning Ability ◽  
Knowledge Level ◽  
Learning Rates ◽  
Learning Capabilities ◽  
The Individual

Education is the vital parameter of the country for development in divergent areas like cultivation, economic, political, health and so on. Any educational Institute’s (universities, colleges, schools) main goal is to increase the student’s learning capabilities and their skills for their full contribution towards the society. In these days, “student’s learning process and skill development” research topic requires much needed attention for the betterment of the society. The student’s performance depends on his/her learning ability and is influenced by many factors. In this paper, we analyze the different categories of student’s leanings that are very fast, fast, moderate and slow. For this, we conducted the training and tests for attributes like ability, knowledge level, reasoning and core subject abilities for the 313 engineering students in AITAM, Tekkali, affiliated to JNTUK, India from 2017 to 2019. We gathered information about personal, academic, cognitive level and demographic data of students. In this experiment, we are conducting statistical analysis as well as classification of students into 4 types of learners and applying the different Machine Learning (ML) techniques and choose the best ML algorithm for predicting students learning rates. This leads to conducting the remedial classes with new teaching methods for moderate and slow leaning students. The proposed paper accommodates the individual differences of the learners in terms of knowledge level, learning preferences, cognitive abilities etc. For this, we apply 5 ML algorithms that are Naive Bayes, classification Trees (CTs), k-NN, C4.5 and SVM. As per ML analysis, the k-Nearest Neighborhood (k-NN) algorithm is more efficient than other algorithms where the accuracy and prediction values are nearer to 100%.

scholarly journals On Transfer Learning of Traditional Frequency and Time Domain Features in Turning

2020 ◽  
Author(s):  
Melih C. Yesilli ◽  
Firas A. Khasawneh
Keyword(s):  
Transfer Learning ◽  
Wavelet Packet ◽  
Cutting Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Learning Ability ◽  
Support Vector ◽  
Depth Of Cut ◽  
Learning Tools ◽  
Mode Decomposition ◽  
Learning Capabilities

Abstract There has been an increasing interest in leveraging machine learning tools for chatter prediction and diagnosis in discrete manufacturing processes. Some of the most common features for studying chatter include traditional signal processing tools such as Fast Fourier Transform (FFT), Power Spectral Density (PSD), and the Auto-correlation Function (ACF). In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment. The experiment is performed using four different tool overhang lengths with varying cutting speed and the depth of cut. We then examine the resulting signals and tag them as either chatter or chatter-free. The tagged signals are then used to train a classifier. The classification methods include the most common algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Gradient Boost (GB). Our results show that features extracted from the Fourier spectrum are the most informative when training a classifier and testing on data from the same cutting configuration yielding accuracy as high as %96. However, the accuracy drops significantly when training and testing on two different configurations with different structural eigenfrequencies. Thus, we conclude that while these traditional features can be highly tuned to a certain process, their transfer learning ability is limited. We also compare our results against two other methods with rising popularity in the literature: Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). The latter two methods, especially EEMD, show better transfer learning capabilities for our dataset.

scholarly journals Brain size affects performance in a reversal-learning test

2018 ◽  
Vol 285 (1871) ◽  
pp. 20172031 ◽  
Author(s):  
Séverine D. Buechel ◽  
Annika Boussard ◽  
Alexander Kotrschal ◽  
Wouter van der Bijl ◽  
Niclas Kolm
Keyword(s):  
Reversal Learning ◽  
Cognitive Complexity ◽  
Poecilia Reticulata ◽  
Brain Size ◽  
Learning Ability ◽  
Colour Discrimination ◽  
Reward Contingency ◽  
Cognitive Benefits ◽  
Learning Test ◽  
Basic Learning

It has become increasingly clear that a larger brain can confer cognitive benefits. Yet not all of the numerous aspects of cognition seem to be affected by brain size. Recent evidence suggests that some more basic forms of cognition, for instance colour vision, are not influenced by brain size. We therefore hypothesize that a larger brain is especially beneficial for distinct and gradually more complex aspects of cognition. To test this hypothesis, we assessed the performance of brain size selected female guppies ( Poecilia reticulata ) in two distinct aspects of cognition that differ in cognitive complexity. In a standard reversal-learning test we first investigated basic learning ability with a colour discrimination test, then reversed the reward contingency to specifically test for cognitive flexibility. We found that large-brained females outperformed small-brained females in the reversed-learning part of the test but not in the colour discrimination part of the test. Large-brained individuals are hence cognitively more flexible, which probably yields fitness benefits, as they may adapt more quickly to social and/or ecological cognitive challenges. Our results also suggest that a larger brain becomes especially advantageous with increasing cognitive complexity. These findings corroborate the significance of brain size for cognitive evolution.

scholarly journals CSNN: An Augmented Spiking based Framework with Perceptron-Inception

2018 ◽  
Author(s):  
Qi Xu ◽  
Yu Qi ◽  
Hang Yu ◽  
Jiangrong Shen ◽  
Huajin Tang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Feature Learning ◽  
Classification Performance ◽  
Learning Ability ◽  
Extraction Ability ◽  
Feature Maps ◽  
Learning Capabilities ◽  
Biological Realism ◽  
High Level

Spiking Neural Networks (SNNs) represent and transmit information in spikes, which is considered more biologically realistic and computationally powerful than the traditional Artificial Neural Networks. The spiking neurons encode useful temporal information and possess highly anti-noise property. The feature extraction ability of typical SNNs is limited by shallow structures. This paper focuses on improving the feature extraction ability of SNNs in virtue of powerful feature extraction ability of Convolutional Neural Networks (CNNs). CNNs can extract abstract features resorting to the structure of the convolutional feature maps. We propose a CNN-SNN (CSNN) model to combine feature learning ability of CNNs with cognition ability of SNNs.  The CSNN model learns the encoded spatial temporal representations of images in an event-driven way. We evaluate the CSNN model on the handwritten digits images dataset MNIST and its variational databases. In the presented experimental results, the proposed CSNN model is evaluated regarding learning capabilities, encoding mechanisms, robustness to noisy stimuli and its classification performance. The results show that CSNN behaves well compared to other cognitive models with significantly fewer neurons and training samples. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this high-level vision task.

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