scholarly journals Deep Active Learning for Classifying Cancer Pathology Reports

2020 ◽  
Author(s):  
Kevin De Angeli ◽  
Shang Gao ◽  
Mohammed Alawad ◽  
Hong-Jun Yoon ◽  
Noah Schaefferkoetter ◽  
...  
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Text Classification ◽  
Random Sampling ◽  
Learning Strategy ◽  
Classification Model ◽  
Cancer Pathology ◽  
Learning Techniques ◽  
Active Learning Strategies ◽  
Pathology Reports

Abstract Background: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of eleven active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network (CNN) as the text classification model. Results: We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions: Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset that constructed using random sampling.

scholarly journals Deep active learning for classifying cancer pathology reports

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kevin De Angeli ◽  
Shang Gao ◽  
Mohammed Alawad ◽  
Hong-Jun Yoon ◽  
Noah Schaefferkoetter ◽  
...  
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Text Classification ◽  
Random Sampling ◽  
Learning Strategy ◽  
Classification Model ◽  
Cancer Pathology ◽  
Learning Techniques ◽  
Active Learning Strategies ◽  
Pathology Reports

Abstract Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

Strategi Pembelajaran Aktif untuk Meningkatkan Pemahaman Materi Kuliah Antar Semester

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Muhammad Zuhdi ◽  
Joni Rokhmat
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Prospective Teachers ◽  
Physics Education ◽  
Learning Strategy ◽  
Cognitive Conflict ◽  
Test Results ◽  
School Year ◽  
Post Test ◽  
Active Learning Strategies

Active Learning Strategies to Improve Understanding of Fundamental Physics Objects. Physics is one of the subjects considered difficult by students. The learning strategies applied by the teacher must be well structured in order to be able to provide understanding to students. Active learning strategies need to be done to improve understanding of physics properly and correctly. This study was conducted to determine the effectiveness of active learning to improve understanding of physics in prospective teachers. This active learning strategy is combined with causalitics and cognitive conflict learning methods which are applied to inter-semester course material at the end of the 2018 – 2019 school year. Learning with this combination of methods is able to provide a good understanding of existing students. The post-test results, which were compared with the pre-test results, showed an increase in the average student understanding of up to 24%. Active learning is proven to be able to improve the understanding of lecture material for students.  Keywords: active learning, physics education

Active Learning Strategies for Online College Classrooms

2020 ◽  
pp. 1-19
Author(s):  
Marina Kamenetskiy
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Educational Leaders ◽  
Learning By Doing ◽  
Learning Activities ◽  
College Classrooms ◽  
Learning Techniques ◽  
Online Classrooms ◽  
Active Learning Strategies ◽  
Interest And Motivation

The term active learning is also known as “learning by doing”; it is where students are presented with a variety of learning activities that encourages thinking and reflection. Educational leaders recognize the value of promoting active learning in the educational setting and encourage their faculty to apply active learning techniques in their online classrooms to increase learner interest and motivation. This chapter identifies various active learning strategies that can be applied to any discipline in any online course, as well as presents different examples of active learning activities. Active learning strategies can include group work, simulations (role play), and games, in order to build learners' critical thinking, problem-solving, and collaboration skills.

Assessment of Active Learning

2011 ◽  
Author(s):  
Kay Gibson ◽  
Carolyn M. Shaw
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Traditional Teaching ◽  
Video Content ◽  
Self Assessment ◽  
Learning Techniques ◽  
Traditional Teaching Methods ◽  
Measurement Tools ◽  
Active Learning Strategies ◽  
Assessment Techniques

With the shift in learning objectives that were more focused on the development of skills and processes, new assessment techniques were required to be developed to determine the effectiveness of new active-learning techniques for teaching these skills. In order for assessment to be done well, instructors must consider what learning objective they are assessing, clarify why they are assessing and what benefits will derive from the process, consider whether they will conduct assessments during or after the learning process, and specifically address how they will design solid assessments of active learning best suited to their needs. The various types of assessment for active-learning strategies include written and oral debriefing, observations, peer- and self-assessment, and presentations and demonstrations. In addition, there are several different measurement tools for recording the assessment data, including checklists and student surveys. A final aspect to consider when examining assessment techniques and measurement tools is the construction of an effective rubric. Ultimately, further research is warranted in the learning that occurs through the use of active-learning techniques in contrast with traditional teaching methods, the “portability” of active-learning exercises across cultures, and the use of newer media—such as internet and video content—as it is increasingly incorporated into the classroom.

scholarly journals Are the Sequential Interactive Effects of Two Active Learning Strategies Synergistic? The Use of the Socratic Method of Questioning and Ability Based Learning Techniques to Enhance Student Learning

2019 ◽  
Author(s):  
La Shun L. Carroll
Keyword(s):  
Active Learning ◽  
Student Learning ◽  
Learning Strategies ◽  
Course Design ◽  
Well Being ◽  
Interactive Effects ◽  
Gainful Employment ◽  
Learning Techniques ◽  
Active Learning Strategies ◽  
Enhance Student Learning

If students do not fully apply themselves, then they may be considered responsible for the result of being inadequately prepared. +- Nevertheless, student outcomes are more likely to reflect a combination of both effort and systematic problems with overall course architecture. Deficiencies in course design result in inadequate preparation that adversely and directly impacts students’ productivity upon entering the workforce.  Such an impact negatively influences students' ability to maintain gainful employment and provide for their families, which inevitably contributes to the development of issues concerning their psychological well-being.  It is well-documented that incorporating active learning strategies in course design and delivery can enhance student learning outcomes.  Despite the benefit of implementing active learning techniques, rarely in the real world will it be possible for techniques to be used in isolation of one another.  Therefore, the purpose of this proposed study is to determine the interactive effects of two active learning strategies because, at a minimum, technique-pairs more accurately represent the application of active learning in the natural educational setting.  There is a paucity of evidence in the literature directed toward investigating the interactive effects of multiple active learning techniques that this study is aimed at filling.  The significance of this research is that, by determining the interactive effects of paired active learning strategies, other research studies on the beneficial effects of using particular active learning technique-pairs will be documented contributing to the literature so that ultimately classroom instruction may be customized according to the determination of optimal sequencing of strategy-pairs for particular courses, subjects, and desired outcomes that maximize student learning.

scholarly journals THE EFFECT OF ACTIVE LEARNING STRATEGY USING THE THREE STAGE OF FISHBOWL DECISION TYPES ON THE RESULT OF HISTORY LEARNING

2013 ◽  
Vol 14 (1) ◽  
pp. 101
Author(s):  
Wahidul Basri
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Design Research ◽  
Learning Strategy ◽  
Control Group ◽  
Control Group Design ◽  
Active Learning Strategies ◽  
Historical Fact ◽  
Good For ◽  
Active Learning Strategy

The ability of students of SMAN 1 Bukittinggi in studying history, especially in interpreting the facts are still low. To overcome these problems the writer try to applied active learning strategies Three Stage of Fishbowl Decision types (TSFD). The purpose of this study is to determine the effect of the use of active learning strategies on learning outcomes of TSFD type in the result of studying history, especially in the interpretation of historical facts. This research is experimental with Pretest-Posttest Control Group Design research. The results showed that active learning strategies of the type of TSFD were good for interpreting historical fact. However, after further analysis based on the pattern of growth or movement changes, in the process of active learning strategies of TSFD type was suitable to be applied. Furthermore, for the facts are based on royal topics of TSFD strategy that has turned out as good for the material that is repetition. Based on the analysis conducted it is believed that active learning strategies of TSFD type is better used on materials that complete require repetition.

scholarly journals A STUDY REVIEW OF ACTIVE LEARNING STRATEGY IN IMPROVING STUDENTS' LEARNING MOTIVATION

2020 ◽  
Vol 1 (1) ◽  
pp. 8-15
Author(s):  
Ubabuddin
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Learning Process ◽  
Teaching And Learning ◽  
Learning Strategy ◽  
Research Question ◽  
Problem Formulation ◽  
Learning Approaches ◽  
Active Learning Strategies ◽  
Active Learning Strategy

Scope: Learning approaches that are considered effective and feasible to be applied in the current learning process are active learning strategies. By using an active learning strategy, students will be invited to always be involved and motivated to do their best in each learning process, so that students will become excited in participating in learning. Objective: This qualitative study presented the results of literature reviewed gathering from various theories, including national, international journals, books, internet and other literature to answer the problem formulation. Method: A serial of literature on active learning strategies that actively applied in most modern education were reviewed and presented to answer the research question. Findings: based on many experts in the fields of teaching and learning, the findings of this study were First, Active learning starts with questions, card short, the power of two, jigsaw, Index card match, picture and picture, cooperative script, problem based instruction, students team achievement devision, etc. Significance: The findings of this literature rewiewing has promoted students better thoughtful and understanding on material presented as participants engaging themselves with the lesson cores not simply just follow teacher's instruction. These findings are also so useful insight to keep student's concentration and improving learning achieving to the higher learning outcomes as demanded by instructional curriculum. Recomendation: Monotonous and teacher-focused learning is increasingly in demand and continues to be abandoned because it makes students bored and boring. With an effective approach it is hoped that learning objectives can be optimally achieved.

Active learning strategies for solving the cold user problem in model-based recommender systems

2021 ◽  
pp. 1-15
Author(s):  
Tomas Geurts ◽  
Stelios Giannikis ◽  
Flavius Frasincar
Keyword(s):  
Active Learning ◽  
Recommender Systems ◽  
Learning Strategies ◽  
Matrix Factorization ◽  
Learning Strategy ◽  
Choice Overload ◽  
Model Based ◽  
Active Learning Strategies ◽  
Product Recommendations ◽  
Active Learning Strategy

Customers of a webshop are often presented large assortments, which can lead to customers struggling finding their desired product(s), an issue known as choice overload. In order to overcome this issue, recommender systems are used in webshops to provide personalized product recommendations to customers. Though, model-based recommender systems are not able to provide recommendations to new customers (i.e., cold users). To facilitate recommendations to cold users we investigate multiple active learning strategies, and subsequently evaluate which active learning strategy is able to optimally elicit the preferences from the cold users in a matrix factorization context. Our model is empirically validated using a dataset from the webshop of de Bijenkorf, a Dutch department store. We find that the overall best-performing active learning strategy is PopError, an active learning strategy that measures the variance score for each item.

scholarly journals STRATEGI PEMBELAJARAN AKTIF QUESTION STUDENT HAVE (QSH) PADA MATA PELAJARAN FIQH DI MTS NEGERI 9 BANTUL

2020 ◽  
Vol 5 (1) ◽  
pp. 84-102
Author(s):  
Ida Zusnani ◽  
Ali Murfi
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Learning Goals ◽  
Learning Material ◽  
Classroom Situation ◽  
Specific Time ◽  
Active Learning Strategies ◽  
Qualitative Descriptive ◽  
Descriptive Method

Learning that does not pay attention to individual differences in children and is based on the wishes of the teacher, will be challenging to be able to lead students towards the achievement of learning goals. One application rather than active learning is the Question Students Have strategy (questions from students). This study aims to find out and understand the forms, steps, strengths, and weaknesses of the Question Student Have (QSH) learning strategy, as well as how the Learning Implementation Plan (RPP) is modeled in Fiqh subjects in MTs Negeri 9 Bantul. This research uses a qualitative descriptive method. The results showed that the form of the Question Student Have strategy teachers stimulated students to learn firsthand the learning material materials that would be delivered within a specific time. After that, students are invited to submit questions from material that they do not understand nor understand. Steps to make the Question Student Have strategy more effective then allocate time for each session after that if the class is too large so that there is not enough time to distribute the paper to all students, divide the class into groups and follow the instructions as above. This Question Student Have a strategy that can attract and focus the student's attention even though the classroom situation was complicated before or students had a joking habit during the lesson. However, not all students were comfortable with making questions because the level of students' abilities in the class was different. In the future, a teacher must continue to look for and formulate strategies that can embrace all differences held by students. Keywords: Active Learning Strategies, Question Student Have (QSH), Fiqh Subjects, MTs Negeri 9 Bantul.

scholarly journals Increasing youth awareness of local culture through active learning

2021 ◽  
Vol 16 (4) ◽  
pp. 1582-1601
Author(s):  
Silvia Rosa ◽  
Ivonne Olivia ◽  
Satya Gayatri ◽  
Tira Nur Fitria ◽  
Ahmad Ridho Rojabi
Keyword(s):  
Active Learning ◽  
Learning Outcomes ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Role Play ◽  
Student Learning Outcomes ◽  
Local Culture ◽  
Participatory Techniques ◽  
Active Learning Strategies ◽  
Active Learning Strategy

This study aims to determine the influence of practice-based active learning on students' interest and response in learning local culture in drama classes. The research was conducted at public universities in Indonesia using two active learning strategies. Qualitative methods using participatory techniques, interviews, and observations were carried out in collecting data for this study. The sample of this research is fifty drama class students. The analysis of data was done after the drama classes ended, which was marked by the process of assessing student learning outcomes through stage performances. This study showed a statistically significant increase in students' interest and response to learning local culture through collaborative learning methods and role-play in drama classroom learning. This study recommends adopting an active learning strategy in teaching local cultural materials to students. Further research is recommended on designing different active learning strategies with other variables and in different locations.     Keywords: Scriptwriting; classroom drama teaching; teaching local culture; active learning.

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