scholarly journals Foreword

2017 ◽  
Author(s):  
Hector Cancela
Keyword(s):  
Natural Language Processing ◽  
Distance Learning ◽  
Natural Language ◽  
Latin American ◽  
Language Processing ◽  
Learning Analytics ◽  
Student Behavior ◽  
Original Research ◽  
Regular Paper ◽  
Learning Institutions

The 20th volume of the CLEI electronic journal includes six papers selected from an open call for papers on Natural Language Processing (NLP), processed by invited editors Gerardo Sierra and César Aguilar, and an additional regular paper. The NLP subject was suggested by Gabriela Marín and Antonio Leoni de León, the chairs of NLPCR 2016, the First Costa-Rican Colloquium on Natural Language Processing. The invited editors took care of the review process and the selection of the six papers, as discussed in their Preface which opens this issue. The additional regular paper is a contribution by Jose Aguilar, Guido Riofrío, and Eduardo Encalada, titled "Learning Analytics focused on student behavior. Case study: dropout in distance learning institutions", which focuses on the use of Learning Analytics for undestanding student behavior in the context of distance learning universities, particularly focuses on the students’ behavior, with the goal of identifying factors that influence the decision of a student to abandon their studies, predicting students susceptible to abandon their studies, and defining their motivational patterns. This issue, which opens the 20th volume of the journal, is a mark of the success of the mission of CLEIej as a vehicle for publishing Latin American based original research in subjects of international interest; we are glad to be able to develop this mission with the support of the researchers who kindly contribute their time as invited editors and reviewers of the papers received.

scholarly journals Learning Analytics focused on student behavior. Case study: dropout in distance learning institutions

2017 ◽  
Author(s):  
Jose Aguilar ◽  
Guido Riofrío ◽  
Eduardo Encalada
Keyword(s):  
Distance Learning ◽  
Learning Process ◽  
Business Intelligence ◽  
Learning Analytics ◽  
Student Behavior ◽  
University Studies ◽  
Educación A Distancia ◽  
Learning Institutions ◽  
Learning Analysis

Abstract: Normally, Learning Analytics (LA) can be focused on the analysis of the learning process or the student behavior. In this paper is analyzed the use of LA in the context of distance learning universities, particularly focuses on the students’ behavior. We propose to use a new concept, called "Autonomic Cycle of Learning Analysis Tasks", which defines a set of tasks of LA, whose common objective is to achieve an improvement in the process under study. In this paper, we develop the "Autonomic Cycle of LA Tasks" to analyze the dropout in distance learning institutions. We use a business intelligence methodology in order to develop the "Autonomic Cycle of LA Tasks" for the analysis of the dropout in distance learning. The Autonomic Cycle identifies factors that influence the decision of a student to abandon their studies, predicts the potentially susceptible students to abandon their university studies, and define a motivational pattern for these students.  Spanish Abstract: Normalmente, La Análitica del Aprendizaje puede enfocarse en el análisis del proceso de aprendizaje, o en el análisis del comportamiento del estudiante. En este artículo se analiza el uso de LA en el contexto de las universidades a distancia, centrándonos particularmente en el comportamiento de los estudiantes. Para ello, proponemos utilizar un nuevo concepto, llamado "Ciclo Autonómico de Tareas de Análitica del Aprendizaje", que define un conjunto de tareas de LA, cuyo objetivo común es lograr una mejora en el proceso bajo estudio. En este artículo se desarrolla el "Ciclo Autonómico de Tareas LA" para analizar la deserción estudiantil en las instituciones de educación a distancia. Para ello, utilizamos una metodología de inteligencia de negocios con el fin de desarrollar dicho ciclo para el análisis de la deserción en el aprendizaje a distancia. El Ciclo Autonómico identifica factores que influyen en la decisión del estudiante de abandonar sus estudios universitarios, predice los estudiantes potencialmente susceptibles a desertar, y define un patrón de motivación para estos estudiantes.

scholarly journals Editorial Note

2010 ◽  
Vol 16 (1) ◽  
pp. 1-2
Author(s):  
Ruslan Mitkov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Industrial Applications ◽  
Editorial Note ◽  
Original Research ◽  
Language Engineering ◽  
Practical Applications ◽  
Market Opportunities

Natural Language Engineering (NLE) enters the second decade of the twenty-first century having established itself as a leading forum for high-quality articles covering all aspects of applied Natural Language Processing research, including, but not limited to, the engineering of natural language methods and applications. It continues to promote first class original research and bridge the gap between traditional computational linguistics research and the implementation of practical applications with potential real-world use. The journal has responded in several ways to the ongoing interest in and growth of research in this area. In 2007 NLE increased its number of pages per issue, thus enabling the publication of more articles. As of January 2010, new publication types are also promoted. In addition to welcoming articles which report on original, unpublished research, the journal now invites surveys presenting the state of the art in important areas of Natural Language Engineering and Natural Language Processing (such as tasks, tools, resources or applications) as well as squibs discussing specific problems. Book reviews and reports on industrial applications will continue to have a prominent place in the Journal. Conference reports, comparative discussions of Natural Language Engineering products and policy-orientated papers examining, for example, funding programmes or market opportunities, are welcome too. Special issues will remain an important feature of the Journal. We envisage one special issue per year, on average. Special issues are selected on a competitive basis after regular calls for proposals.

Natural Language Processing and Learning Analytics

2017 ◽  
pp. 93-104 ◽  
Author(s):  
Danielle S. McNamara ◽  
◽  
Laura K. Allen ◽  
Scott A. Crossley ◽  
Mihai Dascalu ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Analytics

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.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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