Challenges Facing Teachers in Integrating Educational Technology into Kiswahili Teaching. A Case of Selected Secondary Schools in Kisii County, Kenya.
Bibliographic record
Abstract
Information and Communication Technology (ICT) has brought about profound changes in this 21st century era.ICT has changed the way people communicate and do business. In education, the role of ICT and whether or not it positively influences the learners’ attitudes to work and particularly in language (Kiswahili) has been a matter of much debate. Globally, Kiswahili is taught as a language in universities such as Harvard, Yale, Germany, Osaka-Japan, China, South Korea, South Africa, Ghana and Nigeria just to mention a few. Further, the African Union meetings recognize Kiswahili as one of the languages of communication. The use of ICT creates an environment which moves away from the traditional teacher-centered approaches that have been devoid of learner enjoyment and explorativeness which are important characteristics of effective and meaningful learning. ICT allows learners to create, collect, store, use knowledge and information; and it enables learners to connect with people and resources all over the world (Alberta Learning, 2000). The emphasis of teaching Kiswahili language in Kenya is becoming commonplace. The professional development of teachers on the use of ICT enables them develop and update themselves on the ever changing trends and techniques of integrating Educational Technology (ICT-based ) in teaching. The Ministry of Education in Kenya as in many countries in the world realized and accepted the importance of ICT in teaching. It was with this regard that New Partnership for Africa Development (NEPAD) a pilot project was started with an aim of trying to find out the possibility of realizing the dream of integrating ICT in teaching in secondary schools. However, like any new project, there is a possibility of certain challenges such as students’ attitudes and how to impart knowledge and skills which may first need to be addressed in order to guarantee full implementation and success of the project in Kenyan secondary schools. The presenters of this paper did a study of selected secondary schools in Kisii County Kenya. The purpose of the study was to investigate the professional preparedness of the Kiswahili teachers in integrating educational technology into the teaching of the language and establish challenges teachers face while trying to integrate technology into Kiswahili instructional process. The findings have important implications for the future integration of educational technology in the teaching of Kiswahili in Kenya. Will this dream come true? The presenters will share their findings and experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".