Výsledky vyhledávání - "Tahani Aljohani"

Akademický článek

Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Autor: Tahani Aljohani, Alexandra I. Cristea

Publikováno v: Frontiers in Research Metrics and Analytics, Vol 6 (2021)

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic charac

Externí odkaz: https://doaj.org/article/d65d5a67716b4e49a131c1e3afd6844d

Zobrazit plný text záznamu

A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs

Autor: Laila Alrajhi, Filipe Dwan Pereira, Alexandra I. Cristea, Tahani Aljohani

Publikováno v: Mercedes Rodrigo, Maria & Matsuda, Noburu & Cristea, Alexandra I. & Dimitrova, Vania (Eds.). Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. : Springer, pp. 424-427, Lecture Notes in Computer Science, Vol.13356
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium ISBN: 9783031116469

Deciding upon instructor intervention based on learners’ comments that need an urgent response in MOOC environments is a known challenge. The best solutions proposed used automatic machine learning (ML) models to predict the urgency. These are ‘b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1024aca8bc97dded636fd47e9f822721
http://dro.dur.ac.uk/37036/

Zobrazit plný text záznamu

Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs

Autor: Tahani Aljohani, Alexandra I. Cristea, Laila Alrajhi

Publikováno v: Mercedes Rodrigo, Maria & Matsuda, Noburu & Cristea, Alexandra I. & Dimitrova, Vania (Eds.). Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. : Springer, pp. 396-399, Lecture Notes in Computer Science, Vol.13356
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium ISBN: 9783031116469

Automatically identifying the learner gender, which serves as this paper’s focus, can provide valuable information to personalised learners’ experiences in MOOCs. However, extracting the gender from learner-generated data (discussion forum) is a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::867bab6500b860cf22de256eb19f6c3e
http://dro.dur.ac.uk/37033/1/37033.pdf

Zobrazit plný text záznamu

Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification

Autor: Tahani Aljohani, Alexandra I. Cristea

Publikováno v: Cristea, Alexandra I. & Troussas, Christos (Eds.). Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings. : Springer, pp. 136-147, Lecture Notes in Computer Science, Vol.12677(12677)
Intelligent Tutoring Systems ISBN: 9783030804206
ITS

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. These platforms also bring incredible diversity of learners in terms of their traits. A research

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc23eaf987f3e4517a03b8a26f94212e
https://doi.org/10.1007/978-3-030-80421-3_17

Zobrazit plný text záznamu

Author Profiling: Prediction of Learners' Gender on a MOOC Platform Based on Learners' Comments

Autor: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the boo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56af5f88f0668f2eb5314f92c6eb0195

Zobrazit plný text záznamu

A Secure and Privacy-Preserving Data Collection (SPDC) Framework for IoT Applications

Autor: Tahani Aljohani, Ning Zhang

Publikováno v: Critical Information Infrastructures Security ISBN: 9783030582944
CRITIS

Mobile patient monitoring systems monitor and treat chronic diseases by collecting health data from wearable sensors through mobile devices carried out by patients. In the future, these systems may be hosted by a third-party service provider. This wo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0f2236d91de6607d05f54823b6311b52
https://doi.org/10.1007/978-3-030-58295-1_7

Zobrazit plný text záznamu

Prediction of Users’ Professional Profile in MOOCs Only by Utilising Learners’ Written Texts

Autor: Filipe Dwan Pereira, Alexandra I. Cristea, Tahani Aljohani, Elaine Harada Teixeira de Oliveira

Publikováno v: Intelligent Tutoring Systems ISBN: 9783030496623
ITS

Identifying users’ demographic characteristics is called Author Profiling task (AP), which is a useful task in providing a robust automatic prediction for different social user aspects, and subsequently supporting decision making on massive informa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a39b16ac904120e2f3c653d18faf9a56
https://doi.org/10.1007/978-3-030-49663-0_20

Zobrazit plný text záznamu

Designing a Secure and Privacy-Preserving Data Collection (SPDC) Framework for Collecting Data from Mobile Patients

Autor: Tahani Aljohani, Ning Zhang

Publikováno v: EasyChair Preprints.

Third-party based mobile health monitoring systems are vulnerable to threats not only imposed by outsiders but also authorized insiders, e.g. employees of the third-party service provider. This paper examines issues in this context and proposes a nov

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f82911800e02bdfd8b0a316afe30c9b4
https://doi.org/10.29007/4hjc

Zobrazit plný text záznamu

Predicting Learners' Demographics Characteristics

Autor: Tahani Aljohani, Alexandra I. Cristea

Publikováno v: Proceedings of the 2019 4th International Conference on Information and Education Innovations - ICIEI 2019.

Author Profiling (AP), which aims to predict an author's demographics characteristics automatically by using texts written by the author, is an important mechanism for many applications, as well as highly challenging. In this research, we analyse var

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2935a3a3f6514a7d640e4624fc9e33ef
https://doi.org/10.1145/3345094.3345119

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání