3 research outputs found
Extended Parallel Corpus for Amharic-English Machine Translation
This paper describes the acquisition, preprocessing, segmentation, and
alignment of an Amharic-English parallel corpus. It will be useful for machine
translation of an under-resourced language, Amharic. The corpus is larger than
previously compiled corpora; it is released for research purposes. We trained
neural machine translation and phrase-based statistical machine translation
models using the corpus. In the automatic evaluation, neural machine
translation models outperform phrase-based statistical machine translation
models.Comment: Accepted to 2nd AfricanNLP workshop at EACL 202
A blended learning approach for teaching computer programming : design for large classes in Sub-Saharan Africa
The challenge of teaching programming in higher education is
complicated by problems associated with large class teaching, a prevalent
situation in many developing countries. This paper reports on an investigation
into the use of a blended learning approach to teaching and learning
of programming in a class of more than 200 students. A course and learning
environment was designed by integrating constructivist learning models
of Constructive Alignment, Conversational Framework and the Three-
Stage Learning Model. Design science research is used for the course
redesign and development of the learning environment, and action
research is integrated to undertake participatory evaluation of the intervention.
The action research involved the Students’ Approach to Learning survey,
a comparative analysis of students’ performance, and qualitative data
analysis of data gathered from various sources. The paper makes a theoretical
contribution in presenting a design of a blended learning solution for
large class teaching of programming grounded in constructivist learning
theory and use of free and open source technologies.NORAD project of Hawassa University from the third phase of a Norwegian Government-supported project.http://www.tandfonline.com/loi/ncse20hb201