FIRST YEAR LIFE SCIENCE STUDENTS DEEP LEARNING APPROACH: A PRELIMINARY REPORT

2018 ◽  
Author(s):  
Miguel Leiva-Brondo ◽  
Jaime Cebolla-Cornejo ◽  
Rosa Peiró ◽  
Ana María Pérez-de-Castro
Keyword(s):  
Deep Learning ◽  
Preliminary Report ◽  
Life Science ◽  
First Year ◽  
Learning Approach ◽  
Science Students

scholarly journals COMPARISON STUDENT LEARNING APPROACH OF FIRST YEAR LIFE SCIENCE STUDENTS

2020 ◽  
Author(s):  
Miguel Leiva-Brondo ◽  
Jaime Cebolla-Cornejo ◽  
Rosa Peiró ◽  
Ana María Pérez-de-Castro
Keyword(s):  
Student Learning ◽  
Life Science ◽  
First Year ◽  
Learning Approach ◽  
Science Students

EVALUATION OF COVID 19 IMPACT ON THE LEARNING STRATEGIES OF HEALTH SCIENCE STUDENTS: A SURVEY

2021 ◽  
pp. 72-75
Author(s):  
Padmanabhan Rajesh ◽  
Amalendu Vijay ◽  
Shankar Sethuraman ◽  
PV Sai Karthik ◽  
Prakhar Rustagi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Negative Correlation ◽  
Health Science ◽  
Learning Approach ◽  
Science Students ◽  
Strategic Learning ◽  
Indian States ◽  
Health Science Students ◽  
South Indian ◽  
Surface Learning

Background: The lockdown period following the COVID 19 pandemic has affected students in many ways. The present study aims to investigate any changes in learning practice during this pandemic. We conducted this study to investigate any variations in the learning approach by health science students during the Covid 19 pandemic and also to assess possible confounding factors on learning. Methods: A survey was conducted on 630 health science students from South Indian states through 2 pre-validated questionnaires - Approaches and Study Skills Inventory for Students (ASSIST) and Generalised Anxiety Disorder Assessment (GAD 7), to assess learning approach and anxiety levels respectively. Another set of questionnaire consisting 10 questions were prepared that may affect learning approach. These questionnaires were shared via Google forms across various health science institutions of South India. Results: A signicant decrease in strategic and deep learning scores and increase in surface learning scores were observed after Covid 19 pandemic. Anxiety scores were increased after pandemic. A signicant negative correlation was observed between change in deep learning scores vs change in anxiety scores and change in strategic scores vs change anxiety scores. A positive correlation was observed between change in surface learning scores vs anxiety scores. Decrease in strategic and deep learning scores were signicantly correlated with students perceptions on worsening study environment, decreased effectiveness of academic assessments, decreased time devoted for studies, decreased ability to gure out high yielding questions and a decreased ability to frame quality answers. Increase in surface learning negatively correlated with worsening study environment and decreased study time. A negative correlation was observed between suitable study environment and change in anxiety scores. Conclusion: Increased anxiety level was associated with decrease in deep and strategic learning and increase in surface learning approach after Covid 19 pandemic. Appropriate measures are essential to improve students' academic performance

scholarly journals First-Year Life Science Students’ Understanding of the Role of Plants in the Ecosystem—A Concept Network Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 369
Author(s):  
Ilona Södervik ◽  
Maija Nousiainen ◽  
Ismo. T. Koponen
Keyword(s):  
Network Analysis ◽  
Life Science ◽  
First Year ◽  
Analysis Tool ◽  
Science Students ◽  
Analysis Method ◽  
Language Of Science ◽  
University Life ◽  
Verbal Problem

The purpose of this study is to increase the understanding about undergraduate life science students’ conceptions concerning the role of photosynthesizing plants in the ecosystem, utilizing a network analysis method. Science learning requires the integration and linking of abstract and often counterintuitive concepts successfully into multifaceted networks. The quality of these networks, together with their abilities to communicate via the language of science, influences students’ success in academic, verbal problem-solving tasks. This study contributes to investigating students’ understanding, utilizing a modern network analysis method in exploring first-year university life science students’ written answers. In this study, a total of 150 first-year life science students answered two open-ended tasks related to the role of photosynthesizing plants in the ecosystem. A network analysis tool was used in exploring the occurrence of different-level science concepts and the interrelatedness between these concepts in students’ verbal outputs. The results showed that the richness of concept networks and students’ use of macro-concepts were remarkably varied between the tasks. Higher communicability measures were connected to the more abundant existence of macro-concepts in the task concerning the role of plants from the food-chain perspective. In the answers for the task concerning the role of plants regarding the atmosphere, the students operated mainly with single facts, and there were only minor interconnections made between the central concepts. On the basis of these results, the need for more all-encompassing biology teaching concerning complex environmental and socio-economic problems became evident. Thus, methodological and pedagogical contributions are discussed.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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