scholarly journals Assessing Critical Thinking in Open-ended Answers: An Automatic Approach

2020 ◽  
pp. 109-116
Author(s):  
Antonella Poce ◽  
Francesca Amenduni ◽  
Carlo De Medio ◽  
Alessandra Norgini
Keyword(s):  
Critical Thinking ◽  
Language Processing ◽  
Research Question ◽  
Critical Evaluation ◽  
Weighted Kappa ◽  
Automated Scoring ◽  
Data Set ◽  
Constructed Response ◽  
Human Evaluation

The role of Higher Education (HE) is growingly acknowledged for the promotion of Critical Thinking (CT). Constructed-response tasks (CRT) are recognized to be necessary for the CT assessment, though they present problems related to scoring quality and cost (Ku, 2009). Researchers (Liu, Frankel, Roohr, 2014) have proposed using automated scoring to address the above concerns. The present work is aimed at comparing the features of different Natural Language Processing (NLP) techniques adopted to improve the reliability of a prototype designed to automatically assess six sub-skills of CT in CRT: use of language, argumentation, relevance, importance, critical evaluation and novelty (Poce, 2017). We will present the first (1.0) and the second (2.0) version of the CT prototype and their respective reliability results. Our research question is the following: Which level of reliability are shown respectively by the 1.0 and 2.0 automatic CT assessment prototype compared to expert human evaluation? Data collection is realized in two moments, to measure respectively the CT prototype 1.0 and 2.0 reliability from a total of 264 participants and 592 open-ended answers. Two human assessors rated all of these responses on each of the subskills on a scale of 1-5. Similarly, NLP approaches are adopted to compute a feature on each dimension. Quadratic Weighted Kappa and Pearson product-moment correlation were used to evaluate the between-human agreement and human-NLP agreement. Preliminary findings based on the first data set suggest adequate level of between-human rating agreement and a lower level human-NLP agreement (r .43 for the subscales of Relevance and Importance). We are continuing the analysis of the data collected in the 2nd step and expect to complete them in June 2020.

scholarly journals First Steps Towards NLP-based Formative Feedback to Improve Scientific Writing in Hebrew

2020 ◽  
Author(s):  
Moriah Ariely ◽  
Tanya Nazaretsky ◽  
Giora Alexandron
Keyword(s):  
Science Education ◽  
Language Processing ◽  
Learning Algorithm ◽  
Research Question ◽  
School Science ◽  
Scientific Writing ◽  
Great Promise ◽  
High School Science ◽  
Data Set ◽  
K 12

As scientific writing is an important 21st century skill, its development is a major goal in high school science education. Research shows that developing scientific writing skills requires frequent and tailored feedback, which teachers, who face large classes and limited time for personalized instruction, struggle to give. Natural Language Processing (NLP) technologies offer great promise to assist teachers in thisprocess by automating some of the analysis. However, in Hebrew, the use of NLP in computer-supported writing instruction was until recently hindered by the lack of publicly available resources. In this paper, we present initial results from a study that aims to develop NLP-based techniques to assist teachers in providing personalized feedback in scientific writing in Hebrew, which might be applicable to otherlanguages as well. We focus on writing inquiry reports in Biology, and specifically, on the task of automatically identifying whether the report contains a properly de?ned research question. This serves as a proof-of-concept of whether we can build a pipeline that identifies major components of the report and match them to a predefined grading rubric. To achieve this, we collected several hundreds of reports, annotated them according to a grading rubric to create a supervised data set, and built a machine-learning algorithm that uses NLP-based features. The results show that our model can accurately identify the research question or its absence.To the best of our knowledge, this is the first paper to report on the application of Hebrew NLP for formative assessment in K-12 science education.

The Role of Reasons in Moral Education

2017 ◽  
Author(s):  
Harvey Siegel
Keyword(s):  
Critical Thinking ◽  
Moral Education ◽  
Critical Evaluation ◽  
Moral Improvement

Does critical thinking play a role in moral education? If so, what might the role of critical thinking, and in particular, the quest for and critical evaluation of reasons, play in moral education? This chapter explores the role of reasons—considerations that purport to support candidate beliefs, judgments, and actions—in moral education. It considers reasons as they relate to six proposed aims of moral education—the moral improvement of student actions, beliefs, thinking/reasoning, habits, character, and sentiments—and argues that reasons can and do play roles, albeit of varying and somewhat indeterminate strengths, in the promulgation of all six of these aims.

On keeping cool: The role of inhibition in bilingual word processing

2018 ◽  
Vol 22 (04) ◽  
pp. 699-700 ◽  
Author(s):  
MATHIEU DECLERCK ◽  
GABRIELA MEADE ◽  
JONATHAN GRAINGER
Keyword(s):  
Language Processing ◽  
Word Processing ◽  
Data Set ◽  
Lexical Representations ◽  
Target Article ◽  
One Step ◽  
Different Levels ◽  
Language Membership ◽  
Target Data

One of the cool aspects of the original implementation of the BIA model (van Heuven, Dijkstra & Grainger, 1998) was the discovery that inhibitory connections between language nodes and lexical representations was a necessary feature for the model to be able to simulate the target data set at that time. This demonstrates the importance of computational modeling, a key point of the present target article, since inhibitory connections were postulated to occur only between representations at the same level in the conceptual model (Grainger & Dijkstra, 1992). Top-down inhibition was subsequently dropped in the BIA+ model (Dijkstra & van Heuven, 2002), and the Multilink model of the present target article (Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte & Rekké, 2018) goes one step further by removing all kinds of inhibitory connections, both between and within levels. Instead, the authors of the model propose that bilingual language processing relies on bidirectional excitatory connections between representations at different levels. This is curious given that even more evidence has accumulated in favor of inhibition since the original implementation of the BIA model, both between neighboring lexical representations (i.e., lateral inhibition) and from language membership representations (e.g., language nodes and tags) down to lexical representations. In this commentary, we focus on whether the exclusion of these two inhibitory processes is warranted, and how the inclusion of these processes might benefit future developments of the model.

scholarly journals Does Tokyo Stock Exchange Appreciate Corporate Innovations? Role of Patents’ Quality and Research Productivity.

2020 ◽  
Author(s):  
Kostiantyn Ovsiannikov
Keyword(s):  
Stock Exchange ◽  
Manufacturing Sector ◽  
Dominant Role ◽  
Research Question ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Data Set ◽  
Empirical Measures ◽  
Tokyo Stock Exchange

This paper examines the market perception of corporate innovations in Japan. It follows the research question formulated by Hall, Jaffe, and Trajtenberg (2005): "how does innovative activity translate into market value, and what aspects of the underlying process are captured by the empirical measures available?". The novelty of my study is twofold. First, it embraces the longitudinal innovation- and finance-related corporate records to come up with the largest ever combined data-set for Japan that encompasses 632 companies listed at the Tokyo Stock Exchange over the period of 19 years. Second, in addition to linear regressions, it applies the generalized additive models (GAMs). The latter technique allows for realistically capturing nonlinear patterns present in the data while at the same time retaining predictive features of a model. The main finding of the article is following. Amid the dominant role of research and development (R&D), especially for the Pharmaceutical and Chemical industries, market consistently rewards influential patents in the manufacturing sector.

scholarly journals Video captioning with stacked attention and semantic hard pull

2021 ◽  
Vol 7 ◽  
pp. e664
Author(s):  
Md. Mushfiqur Rahman ◽  
Thasin Abedin ◽  
Khondokar S.S. Prottoy ◽  
Ayana Moshruba ◽  
Fazlul Hasan Siddiqui
Keyword(s):  
Qualitative Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
The Other ◽  
Video Sequences ◽  
Automated Scoring ◽  
Video Captioning ◽  
Human Evaluation ◽  
Novel Approaches ◽  
Evaluation Metric

Video captioning, i.e., the task of generating captions from video sequences creates a bridge between the Natural Language Processing and Computer Vision domains of computer science. The task of generating a semantically accurate description of a video is quite complex. Considering the complexity, of the problem, the results obtained in recent research works are praiseworthy. However, there is plenty of scope for further investigation. This paper addresses this scope and proposes a novel solution. Most video captioning models comprise two sequential/recurrent layers—one as a video-to-context encoder and the other as a context-to-caption decoder. This paper proposes a novel architecture, namely Semantically Sensible Video Captioning (SSVC) which modifies the context generation mechanism by using two novel approaches—“stacked attention” and “spatial hard pull”. As there are no exclusive metrics for evaluating video captioning models, we emphasize both quantitative and qualitative analysis of our model. Hence, we have used the BLEU scoring metric for quantitative analysis and have proposed a human evaluation metric for qualitative analysis, namely the Semantic Sensibility (SS) scoring metric. SS Score overcomes the shortcomings of common automated scoring metrics. This paper reports that the use of the aforementioned novelties improves the performance of state-of-the-art architectures.

Electron Microscopy of Metal-Matrix Composites

1970 ◽  
Vol 28 ◽  
pp. 404-405
Author(s):  
A. Lawley ◽  
M. R. Pinnel ◽  
A. Pattnaik
Keyword(s):  
Electron Microscopy ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Critical Evaluation ◽  
Elastic Behavior ◽  
Matrix Composites ◽  
Directionally Solidified ◽  
Fracture Characteristics ◽  
Broad Program

As part of a broad program on composite materials, the role of the interface on the micromechanics of deformation of metal-matrix composites is being studied. The approach is to correlate elastic behavior, micro and macroyielding, flow, and fracture behavior with associated structural detail (dislocation substructure, fracture characteristics) and stress-state. This provides an understanding of the mode of deformation from an atomistic viewpoint; a critical evaluation can then be made of existing models of composite behavior based on continuum mechanics. This paper covers the electron microscopy (transmission, fractography, scanning microscopy) of two distinct forms of composite material: conventional fiber-reinforced (aluminum-stainless steel) and directionally solidified eutectic alloys (aluminum-copper). In the former, the interface is in the form of a compound and/or solid solution whereas in directionally solidified alloys, the interface consists of a precise crystallographic boundary between the two constituents of the eutectic.

PERAN ORANGTUA DAN GURU PEMBIMBING KHUSUS KEPADA ANAK BERKUBUTAHAN KHUSU (SLOW LEARNER) DI SD NEGERI 5 ARCAWINANGUN

2019 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Ujang Khiyarusoleh
Keyword(s):  
Special Needs ◽  
Research Question ◽  
Children With Special Needs ◽  
Special Needs Children ◽  
Slow Learner ◽  
Learning Achievement ◽  
Study Approach ◽  
Role Of Parents ◽  
Guidance Teachers

ABSTRAK Penelitian ini dilatarbelakangi oleh adanya pendidikan yang diperuntukkan bagi semua anak, termasuk anak berkebutuhan khusus. Anak berkebutuhan khusus memiliki karakter yang berbeda-beda, khususnya slow learner dalam pembelajaran mengalami keterlambatan dalam memahami materi. Oleh karena itulah diperlukan peran orangtua dan guru pembimbing khusus untuk membantu memberikan pendidikan yang lebih baik sesuai dengan karakternya. Rumusan masalah penelitian ini yaitu bagaimana peran orangtua dan guru pembimbing khusus kepada slow learner di SD Negeri 5 Arcawinangun. Tujuan penelitian ini yaitu untuk mengetahui peran orangtua dan guru pembimbing khusus kepada slow learner di SD Negeri 5 Arcawinangun. Jenis penelitian ini yaitu penelitian kualitatif dengan pendekatan studi kasus. Teknik pengumpulan data yang digunakan adalah observasi, wawancara, dokumentasi dan triangulasi sumber. Hasil penelitian ini menunjukan bahwa terdapat beberapa peran orangtua yaitu meliputi: orangtua sebagai pendamping utama, orangtua sebagai advokat, orangtua sebagai guru, orangtua sebagai diagnostian. Serta peran guru pembimbing khusus yang meliputi: merancang dan melaksanakan program kekhususan, melakukan identifikasi, asesmen dan menyususn program pembelajaran individual, memodifikasi bahan ajar, melakukan evaluasi, dan membuat laporan program dan perkembangan anak berkebutuhan khusus. Dengan peran peran tersebut, maka sebagian besar anak berkebutuhan khusus di SD Negeri 5 Arcawinangun dapat memberikan layanan dengan baik. Saran untuk penelitian ini orangtua senantiasa mendorong anaknya untuk belajar bersungguh-sungguh di rumah dan di skolah, serta menyediakan fasilitas belajar yang mendukung perkembangan pendidikan bagi anaknya. Kata Kunci: peran guru pembimbing khusus, peran orangtua, slow learner   ABSTRACT Background of the study was the existence of education aimed at all children, including children with special needs. Children with special needs have different characters, thus affecting their learning achievement. Therefore, the role of parents and special tutors were needed to help them improve learning achievement. The research question of this research was how the role of parents and special guidance teachers towards learning achievement of children with special needs in SD Negeri 5 Arcawinangun. The focus of this research was the role of parents and special guidance teachers on learning achievement of children with special needs in grades 1, 2 and 3 of SD Negeri 5 Arcawinangun. The purpose of this study was to determine the role of parents and special guidance teachers on the learning achievement of children with special needs in Arcawinangun 5 Public Elementary School. This type of research was qualitative research with a case study approach. Technique of data collection was observation, interviews, documentation and source triangulation. The results of this research indicated that there were several roles of parents, namely: parents as the main companion, parents as advocates, parents as teachers, parents as diagnostics. As well as the role of a special mentor teacher which includes: designing and implementing specific programs, identifying, assessing and arranging individual learning programs, modifying teaching materials, evaluating, and making program reports and development of children with special needs.With this role, most of the children with special needs in SD Negeri 5 Arcawinangun can improve their learning achievement well.Suggestions of this research were parents always encourage their children to study seriously at home and at school, and provide learning facilities that support the development of education for their children. Keywords: role of parents, role of special guidance teachers, slow learner

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

Philosophical and Pedagogical Views of Yu. F. Samarin

2019 ◽  
Vol 54 ◽  
pp. 209-218
Author(s):  
Lev E. Shaposhnikov
Keyword(s):  
National Identity ◽  
Social Development ◽  
Educational Policy ◽  
Critical Evaluation ◽  
Educational Process ◽  
Educational Context ◽  
The Government ◽  
Role Of Religion

The paper analyses the evolution of Yu. Samarin’s ideas from rationalism to “holistic knowledge”. Special attention is paid to the philosopher’s conceptualization of the key role of religion for a nation. The author also examines the scholar’s position concerning the promotion of patriotism as an important impetus for social development. Emphasis is made on analyzing the interaction of universal and national aspects in the educational process, as well as on the value of national identity in the field of humanities. The article also presents Yu. Samarin’s critical evaluation of the government educational policy and his suggestions on increasing its effectiveness. The author notes the relevance of Yu. Samarin’s views for the contemporary philosophical and educational context.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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