مجال التميز | تميز دراسي وبحثي + جوائز تفوقية |
البحوث المنشورة |
|
البحث (1): | |
عنوان البحث: |
Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations |
رابط إلى البحث: | |
تاريخ النشر: |
02/08/2021 |
موجز عن البحث: |
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 characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bidirectional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented ParserInterpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed. |
البحث (2): | |
عنوان البحث: | Author Profiling: Prediction of Learners’ Gender on a MOOC Platform Based on Learners’ Comments
(Extended paper) |
رابط إلى البحث: |
|
تاريخ النشر: |
30/01/2020 |
موجز عن البحث: |
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 booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic – and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stackaugmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models’ results. |
المؤتمرات العلمية |
|
المؤتمر (1): | |
عنوان المؤتمر: |
CIEI 2019: 2019 The 4th International Conference on Information and Education Innovations |
تاريخ الإنعقاد: |
10 – 12/07/ 2019 |
مكان الإنعقاد: |
Durham. UK |
طبيعة المشاركة: |
Oral presentation |
عنوان المشاركة: |
Predicting Learners’ Demographics Characteristics: Deep Learning Ensemble Architecture for Learners’ Characteristics Prediction in MOOCs |
ملخص المشاركة: |
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 various previous machine learning models for AP, with respect to their potential for our research problem. Based on this, we propose a Deep Learning Architecture to predict the demographics characteristics of the learners in MOOCs, incorporating multi-feature representations and ensemble learning methods. Specifically, we employ a novel pipeline, combining the most successful deep learning classifiers, Convolution Neural Networks, Recurrent Neural Networks and Recursive Neural Networks, to learn from a text. Moreover, beside the state-of-the-art training involving character and word-level input, we additionally propose phrase-level input. With this approach, we aim at deepening our understanding of the writing style of learners, and thus, predict the author profile with high accuracy. In this paper, we propose the model and architecture, and report on initial tests of our model on a large dataset from the FutureLearn platform, to predict the demographics characteristics of the learners. |
الرابط: | |
المؤتمر (2): | |
عنوان المؤتمر: |
ICADMA 2020: International Conference on Advanced Data Mining and Applications |
تاريخ الإنعقاد: |
30-31/01/ 2020 |
مكان الإنعقاد: |
Sydney, Australia |
طبيعة المشاركة: |
Oral presentation |
عنوان المشاركة: |
Author Profiling: Prediction of Learners’ Gender on a MOOC Platform Based on Learners’ Comments |
ملخص المشاركة: |
We aim to predict learners’ gender, by using linguistically motivated Deep Learning Architectures, on a Future Learn MOOC platform. We hypothesise that a higher semantic – and syntactic level sentence processing based on linguistics will render a richer text representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure. Additionally, we explore using different word-level encoding functions. We have implemented these methods on our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models’ results. |
الرابط: |
https://waset.org/advanced-data-mining-and-applications-conference-in-january-2020-in-sydney |
المؤتمر (3): | |
عنوان المؤتمر: |
16th International Conference (ITS 2020) |
تاريخ الإنعقاد: |
8–12/06/2020 |
مكان الإنعقاد: |
Athens, Greece |
طبيعة المشاركة: |
Oral presentation |
عنوان المشاركة: |
Prediction of Users’ Professional Profile in MOOCs Only by Utilising Learners’ Written Texts |
ملخص المشاركة: |
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 information systems. For example, in MOOCs, it used to provide personalised recommendation systems for learners. In this paper, we explore intelligent techniques and strategies for solving the task, and mainly we focus on predicting the employment status of users on a MOOC platform. For this, we compare sequential with parallel ensemble deep learning (DL) architectures. Importantly, we show that our prediction model can achieve high accuracy even though not many stylistic text features that are usually used for the AP task are employed (only tokens of words are used). To address our highly unbalanced data, we compare widely used oversampling method with a generative paraphrasing method. We obtained an average of 96.4% high accuracy for our best method, involving sequential DL with paraphrasing overall, as well as per-individual class (employment statuses of users). |
الرابط: |
https://link.springer.com/chapter/10.1007/978-3-030-49663-0_20 |
المؤتمر (4): | |
عنوان المؤتمر: |
17th International Conference (ITS 2021) |
تاريخ الإنعقاد: |
7–11/06/ 2021 |
مكان الإنعقاد: |
Athens, Greece |
طبيعة المشاركة: |
Oral presentation |
عنوان المشاركة: |
Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification |
ملخص المشاركة: |
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 area called Author Profiling (AP in general; here, Learner Profiling (LP)), is to identify such traits about learners, which is vital in MOOCs for, e.g., preventing plagiarism, or eligibility for course certification. Identifying a learner’s trait in a MOOC is notoriously hard to do from textual content alone. We argue that to predict a learner’s academic level, we need to also be using other features stemming from MOOC platforms, such as derived from learners’ actions on the platform. In this study, we specifically examine time stamps, quizzes, and discussions. Our novel approach for the task achieves a high accuracy (90% in average) even with a simple shallow classifier, irrespective of data size, outperforming the state of the art. |
https://link.springer.com/chapter/10.1007/978-3-030-80421-3_17 |
|
جوائز التكريم |
|
الجائزة (1): | |
مسمى الجائزة: |
Best Presentation Award |
الجهة المانحة: |
International Conference on Information and Education Innovations ICIEI 2019 |
تاريخ الجائزة: |
12/07/2019 |
مجال التكريم: |
Best oral Presentation entitled: “Predicting Learners’ Demographics Characteristics: Deep Learning Ensemble Architecture for Learners’ Characteristics Prediction in MOOCs” presented in ICIEI 2019 Conference . |
الجائزة (2): | |
مسمى الجائزة: |
INTERNATIONAL RESEARCH CONFERENCE CERTIFICATE OF BEST PRESENTATION AWARD |
الجهة المانحة: |
International Research Conference IRC 2020 |
تاريخ الجائزة: |
07/02/2020 |
مجال التكريم: |
The student presented an outstanding work entitled: “ Author Profiling: Prediction of Learners’ Gender on a MOOC Platform Based on Learners’ Comments” presented in ICADMA 2020 : XIV. International Conference on Advanced Data Mining and Applications |
تهاني مصلح مسعد الجهني
دكتوراه
العلوم والتقنية
Durham University