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

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

Awjedni: A Reverse-Image-Search Application

The abundance of photos on the internet, along with smartphones that could implement computer vision technologies allow for a unique way to browse the web. These technologies have potential used in many widely accessible and globally available reverse-image search applications. One of these applications is the use of reverse-image search to help people finding items which they're interested in, but they can't name it. This is where Awjedni was born. Awjedni is a reverse-image search application compatible with iOS and Android smartphones built to provide an efficient way to search millions of products on the internet using images only. Awjedni utilizes a computer vision technology through implementing multiple libraries and frameworks to process images, recognize objects, and crawl the web. Users simply upload/take a photo of a desired item and the application returns visually similar items and a direct link to the websites that sell them

Learner Profiling: Demographics Identification Based on NLP, Machine Learning, and MOOCs Metadata

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID- 19 pandemic is rendering these platforms even more necessary. Many types of research are ongoing to improve the learning resources provided to learners via MOOCs. These platforms also bring an incredible diversity of learners in terms of their demographics; thus, much MOOCs research relies on the learners’ demographics data. Traditionally, these data are extracted from pre-course questionnaires that are filled-in by the learners themselves. However, besides introducing potential cognitive overhead (asking learners to fulfil tasks outside of the main purpose of learning), this leads to a clear bias in any research based on these questionnaires. The latter is because only about 10% of the MOOCs learners provide (a given type of) demographics data (with the intersection of all types of demographic data being significantly below 10%), while others do not provide any type of their demographic data. Thus, the population data obtained via questionnaires is not representative of the actual population in the MOOCs. To resolve this issue, a research area called Learner Profiling (LP) is investigated in this thesis. This area naturally extends from a research area called Author Profiling (AP), which aims at identifying traits about authors in different domains. In- stead, LP aims to identify learners’ demographics in the online educational domain. This research specifically focused on identifying the employment status, gender, and academic level of learners in MOOCs. Classifying the employment status of learners was based on the semantic representation of their comments, and comparing the sequential with the parallel ensemble deep learning architecture (Convolutional Neural Networks and Recurrent Neural Networks). This obtained an average high accuracy of 96.3% for the best proposed method; using NLP based approach for balancing the training samples. Additionally, the task of classifying the gender of learners was tackled based on the syntactic knowledge from the learners’ comments. Different tree-structured Long-Short-Term Memory models were compared and, as a result, the researcher proposed a novel version of a bi-directional composition function for existing architectures. In addition, 18 different combinations of word-level encoding and sentence-level encoding functions for this task were compared and evaluated. Based on the results, the novel bi-directional model outperforms all other models and the highest accuracy result among the proposed models is the one based on the combination of Feedforward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% classification accuracy). Next, the learner’s academic level was identified based on training small size - rich data - i.e. not only textual content (data including learner activity data). The researcher argues here that to classify a learner trait from the sparse textual content, researchers need to use additionally other features stemming from the MOOC platform, such as derived from learners’ actions on that platform. Accordingly, time stamps, quizzes, and discussions were examined, as learners’ behavioural data sources for the classification problem. This novel approach for the task achieves a high accuracy (89% on average), even with a simple classifier, irrespective of data size. To conclude, such classification models as used in this thesis show that they can achieve highly accurate results and that pre-course questionnaires to extract the demographic information with a high cognitive overhead could become obsolete

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

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 Bi-directional 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 Parser-Interpreter 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

Predicting Learners' Demographics Characteristics: Deep Learning Ensemble Architecture for Learners' Characteristics Prediction in MOOCs

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