排序方式: 共有3条查询结果,搜索用时 16 毫秒
1
1.
2.
Based on information gleaned from questionnaires and interviews with teachers who engaged in action research as a capstone to their Master's program during the years 1992 through 2001, and on data from these teachers' administrators and colleagues, six assertions are reported. (1) Teachers sustained the ‘inquiry mindset’ gained while learning the processes associated with conducting action research and continued using aspects of the process; however, conducting new projects was less likely. (2) Teachers' sense of professional efficacy was enhanced, even after many years had intervened. (3) Action research had immediate benefits for students but long-range benefits were not determined. (4) Though challenging, teachers perceived conducting action research was professionally valuable. (5) Teachers reported that administrators, although supportive, played passive roles, whereas colleagues were more collaborative during planning and implementing their projects. (6) Teachers described school environments conducive to conducting action research as ones that provide structures for teams to work on mutual goals supported by strong administrative leadership. 相似文献
3.
Classifying Amharic webnews 总被引:1,自引:1,他引:0
Lars Asker Atelach Alemu Argaw Björn Gambäck Samuel Eyassu Asfeha Lemma Nigussie Habte 《Information Retrieval》2009,12(3):416-435
We present work aimed at compiling an Amharic corpus from the Web and automatically categorizing the texts. Amharic is the
second most spoken Semitic language in the World (after Arabic) and used for countrywide communication in Ethiopia. It is
highly inflectional and quite dialectally diversified. We discuss the issues of compiling and annotating a corpus of Amharic
news articles from the Web. This corpus was then used in three sets of text classification experiments. Working with a less-researched
language highlights a number of practical issues that might otherwise receive less attention or go unnoticed. The purpose
of the experiments has not primarily been to develop a cutting-edge text classification system for Amharic, but rather to
put the spotlight on some of these issues. The first two sets of experiments investigated the use of Self-Organizing Maps
(SOMs) for document classification. Testing on small datasets, we first looked at classifying unseen data into 10 predefined
categories of news items, and then at clustering it around query content, when taking 16 queries as class labels. The second
set of experiments investigated the effect of operations such as stemming and part-of-speech tagging on text classification
performance. We compared three representations while constructing classification models based on bagging of decision trees
for the 10 predefined news categories. The best accuracy was achieved using the full text as representation. A representation
using only the nouns performed almost equally well, confirming the assumption that most of the information required for distinguishing
between various categories actually is contained in the nouns, while stemming did not have much effect on the performance
of the classifier.
相似文献
Lemma Nigussie HabteEmail: |
1