About Me
Research Interests
Machine Learning, Natural Language Processing, Data Visualization and Analytics, Big Data.
Publications
2020
Azra Naseem; Kiran Qasim Ali; Audrey Juma; Afroz Sajwani; Basnama Ayaz Khan; Saleem Sayani; Syed Sibte Raza Abidi
Factors enabling and hindering an eLearning programme for nurses and midwives in Afghanistan Journal Article
In: Scholarship of Teaching and Learning in the South, vol. 4, no. 2, pp. 80, 2020, ISSN: 2523-1154.
Abstract | Links | BibTeX | Tags: eLearning, Learning technology
@article{Naseem2020,
title = {Factors enabling and hindering an eLearning programme for nurses and midwives in Afghanistan},
author = {Azra Naseem and Kiran {Qasim Ali} and Audrey Juma and Afroz Sajwani and Basnama Ayaz Khan and Saleem Sayani and Syed Sibte Raza Abidi},
url = {https://sotl-south-journal.net/?journal=sotls&page=article&op=view&path%5B%5D=106},
doi = {10.36615/sotls.v4i2.106},
issn = {2523-1154},
year = {2020},
date = {2020-09-01},
journal = {Scholarship of Teaching and Learning in the South},
volume = {4},
number = {2},
pages = {80},
abstract = {textlessptextgreaterAfghanistan faces an acute shortage of trained healthcare providers. To build capacity of nurses and midwives, in 2014 a private hospital in Afghanistan initiated an eLearning programme to enhance their knowledge and skills. The study was conducted to identify facilitating and hindering factors for the successful implementation of eLearning. Data collection took place between June and September 2016, when seven Maternal and Child Health (MNCH) related eLearning sessions were conducted. The participants were nurses and midwives working in MNCH wards at the research sites in Bamyan, Faizabad and Kandahar, along with the programme planners and facilitators. Data was collected through pre/post and delayed post-tests, observations and questionnaires, semi-structured interviews and documents analysis. The results highlight four major factors as important for the successful implementation of eLearning, namely: curriculum, context, technology and individual. The needs assessment ensured relevance of the sessions to the needs of the participants. However, pedagogy was lecture-based with limited focus on skills development. Poor connectivity and language of instruction posed challenges. eLearning has shown the potential for developing knowledge and skills of nurses and midwives. Clear communication between teams involved in planning and implementation of the programme, technology infrastructure, design of online pedagogy and facilitator readiness are critical for the success of eLearning in low and middle income countries.
Keywords: Health care providers/system, eLearning Programme, Nurses, Midwives, Maternal and child careHow to cite this article:Naseem, A., Ali, K.Q., Juma, A., Sajwani, A., Khan, B.A., Sayani, A. & Abidi, S.S.R. 2020. Factors enabling and hindering an eLearning programme for nurses and midwives in Afghanistan. Scholarship of Teaching and Learning in the South. 4(2): 80-99. https://doi.org/10.36615/sotls.v4i2.106.This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/textless/ptextgreater},
keywords = {eLearning, Learning technology},
pubstate = {published},
tppubtype = {article}
}
textlessptextgreaterAfghanistan faces an acute shortage of trained healthcare providers. To build capacity of nurses and midwives, in 2014 a private hospital in Afghanistan initiated an eLearning programme to enhance their knowledge and skills. The study was conducted to identify facilitating and hindering factors for the successful implementation of eLearning. Data collection took place between June and September 2016, when seven Maternal and Child Health (MNCH) related eLearning sessions were conducted. The participants were nurses and midwives working in MNCH wards at the research sites in Bamyan, Faizabad and Kandahar, along with the programme planners and facilitators. Data was collected through pre/post and delayed post-tests, observations and questionnaires, semi-structured interviews and documents analysis. The results highlight four major factors as important for the successful implementation of eLearning, namely: curriculum, context, technology and individual. The needs assessment ensured relevance of the sessions to the needs of the participants. However, pedagogy was lecture-based with limited focus on skills development. Poor connectivity and language of instruction posed challenges. eLearning has shown the potential for developing knowledge and skills of nurses and midwives. Clear communication between teams involved in planning and implementation of the programme, technology infrastructure, design of online pedagogy and facilitator readiness are critical for the success of eLearning in low and middle income countries. Keywords: Health care providers/system, eLearning Programme, Nurses, Midwives, Maternal and child careHow to cite this article:Naseem, A., Ali, K.Q., Juma, A., Sajwani, A., Khan, B.A., Sayani, A. & Abidi, S.S.R. 2020. Factors enabling and hindering an eLearning programme for nurses and midwives in Afghanistan. Scholarship of Teaching and Learning in the South. 4(2): 80-99. https://doi.org/10.36615/sotls.v4i2.106.This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/textless/ptextgreater
2016
Soude Fazeli; Enayat Rajabi; Leonardo Lezcano; Hendrik Drachsler; Peter Sloep
Supporting Users of Open Online Courses with Recommendations: an Algorithmic Study Proceedings Article
In: Advanced Learning Technologies (ICALT), 2016 IEEE 16th International Conference on, Austin, TX, USA, IEEE , 2016, ISSN: 2161-377X.
Abstract | Links | BibTeX | Tags: Learning technology
@inproceedings{Fazeli2016,
title = {Supporting Users of Open Online Courses with Recommendations: an Algorithmic Study},
author = {Soude Fazeli and Enayat Rajabi and Leonardo Lezcano and Hendrik Drachsler and Peter Sloep
},
doi = {10.1109/ICALT.2016.119},
issn = {2161-377X},
year = {2016},
date = {2016-07-25},
booktitle = {Advanced Learning Technologies (ICALT), 2016 IEEE 16th International Conference on, Austin, TX, USA},
publisher = {IEEE },
abstract = {Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate recommendations. We use data from the OpenU open online learning platform in use by the Open University of the Netherlands to investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. It appears that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system outperforms the classical approaches on prediction accuracy of recommendations in terms of recall.
},
keywords = {Learning technology},
pubstate = {published},
tppubtype = {inproceedings}
}
Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate recommendations. We use data from the OpenU open online learning platform in use by the Open University of the Netherlands to investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. It appears that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system outperforms the classical approaches on prediction accuracy of recommendations in terms of recall.