Outputs (387)

Can Learner Characteristics Predict Their Behaviour on MOOCs? (2018)
Presentation / Conference Contribution
Cristea, A. I., Alamri, A., Alshehri, M., Kayama, M., Foss, J., Shi, L., & Stewart, C. D. (2018). Can Learner Characteristics Predict Their Behaviour on MOOCs?. In 10th International Conference on Education Technology and Computers (119-125). https://doi.org/10.1145/3290511.3290568

Stereotyping is the first type of adaptation in education ever proposed. However, the early systems have never dealt with the numbers of learners that current MOOCs provide. Thus, the umbrella question that this work tackles is if learner characteris... Read More about Can Learner Characteristics Predict Their Behaviour on MOOCs?.

A large-scale category-based evaluation of a visual language for adaptive hypermedia (2018)
Presentation / Conference Contribution
Khan, J., Cristea, A., & Alamri, A. (2018). A large-scale category-based evaluation of a visual language for adaptive hypermedia. In Proceedings of the 2018 the 3rd International Conference on Information and Education Innovations (ICIEI'18) : London, United Kingdom, June 30 - July 02, 2018 (94-98). https://doi.org/10.1145/3234825.3234834

Adaptive Hypermedia (AH) provides a personalised and customised approach, enhancing the usability of hypermedia, by building a model of various qualities of a user and applying this information to adapt the content and the navigation to their require... Read More about A large-scale category-based evaluation of a visual language for adaptive hypermedia.

Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation (2019)
Presentation / Conference Contribution
Tsakalidis, A., Liakata, M., Damoulas, T., & Cristea, A. I. (2019). Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, …N. Hurley (Eds.), Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III (407-423). https://doi.org/10.1007/978-3-030-10997-4_25

Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies. Such approaches have the potential to revolutionise mental h... Read More about Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation.

Towards detection of influential sentences affecting reputation in Wikipedia (2016)
Presentation / Conference Contribution
Zhou, Y., & Cristea, A. (2016). Towards detection of influential sentences affecting reputation in Wikipedia. In W. Nejdl (Ed.), WebSci '16 : Proceedings of the 8th ACM Conference on Web Science (244-248). https://doi.org/10.1145/2908131.2908177

Wikipedia has become the most frequently viewed online encyclopaedia website. Some sentences in Wikipedia articles have direct and obvious impact on people's opinions towards the mentioned named entities. This paper defines and tackles the problem of... Read More about Towards detection of influential sentences affecting reputation in Wikipedia.

Combining heterogeneous user generated data to sense well-being (2016)
Presentation / Conference Contribution
Tsakalidis, A., Liakata, M., Damoulas, T., Jellinek, B., Guo, W., & Cristea, A. (2016). Combining heterogeneous user generated data to sense well-being. In Y. Matsumoto, & R. Prasad (Eds.), Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics : Technical Papers (3007-3018)

In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile ph... Read More about Combining heterogeneous user generated data to sense well-being.

On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs (2018)
Presentation / Conference Contribution
Alshehri, M., Foss, J., Cristea, A. I., Kayama, M., Shi, L., Alamri, A., & Tsakalidis, A. (2018). On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs. In Proceedings of the 2018 the 3rd International Conference on Information and Education Innovations (ICIEI'18) : London, United Kingdom, June 30 - July 02, 2018 (73-77). https://doi.org/10.1145/3234825.3234833

Stereotyping is the first type of adaptation ever proposed. However, the early systems have never dealt with the numbers of learners that current Massive Open Online Courses (MOOCs) provide. Thus, the umbrella question that this work tackles is if le... Read More about On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs.

An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System (2018)
Presentation / Conference Contribution
Alamri, A., Rusby, H., Cristea, A. I., Khan, J., Shi, L., & Stewart, C. (2018). An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System. In Proceedings of the 2018 the 3rd International Conference on Information and Education Innovations (ICIEI'18) : London, United Kingdom, June 30 - July 02, 2018 (57-61). https://doi.org/10.1145/3234825.3234835

Adaptive Educational Hypermedia (AEH) offers more advanced personalisation and customisation features to the field of e-learning compared to the outdated static systems (where every learner is given the same set of learning materials). AEH can improv... Read More about An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System.

Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses (2018)
Presentation / Conference Contribution
Cristea, A., Alamri, A., Kayama, M., Stewart, C., Alsheri, M., & Shi, L. (2018). Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund University

Whilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. I... Read More about Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses.

Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses (2018)
Presentation / Conference Contribution
Shi, L., & Cristea, A. (2018). Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund University

Big data and analytics for educational information systems, despite having gained researchers’ attention, are still in their infancy and will take years to mature. Massive open online courses (MOOCs), which record learner-computer interactions, bring... Read More about Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses.

Towards understanding learning behavior patterns in social adaptive personalized e-learning systems (2013)
Presentation / Conference Contribution
Shi, L., Cristea, A., Awan, M., Stewart, C., & Hendrix, M. (2013). Towards understanding learning behavior patterns in social adaptive personalized e-learning systems. In Hyperconnected World: Anything, Anywhere, Anytime; Proceedings of the 19th Americas Conference on Information Systems (AMCIS 2013) (1-10)

Implicit user modeling has always long since played an important role in supporting personalized web-based e-learning environments and is increasingly important in other learning environments such as serious games. Its main concern is to unobtrusivel... Read More about Towards understanding learning behavior patterns in social adaptive personalized e-learning systems.

Motivational gamification strategies rooted in self-determination theory for social adaptive E-Learning (2016)
Presentation / Conference Contribution
Cristea, A., & Shi, L. (2016). Motivational gamification strategies rooted in self-determination theory for social adaptive E-Learning. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Intelligent Tutoring Systems, 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016, Proceedings (294-300). https://doi.org/10.1007/978-3-319-39583-8_32

This study uses gamification as the carrier of understanding the motivational benefits of applying the Self-Determination Theory (SDT) in social adaptive e-learning, by proposing motivational gamification strategies rooted in SDT, as well as developi... Read More about Motivational gamification strategies rooted in self-determination theory for social adaptive E-Learning.

Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection (2017)
Presentation / Conference Contribution
Zhou, Y., Cristea, A. I., & Shi, L. (2017). Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection. In A. Bouguettaya, Y. Gao, A. Klimenko, L. Chen, X. Zhang, F. Dzerzhinskiy, …Q. Li (Eds.), Web Information Systems Engineering -- WISE 2017 (18-32). https://doi.org/10.1007/978-3-319-68783-4_2

Understanding what people say and really mean in tweets is still a wide open research question. In particular, understanding the stance of a tweet, which is determined not only by its content, but also by the given target, is a very recent research a... Read More about Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection.

WarwickDCS : from phrase-based to target-specific sentiment recognition (2015)
Presentation / Conference Contribution
Townsend, R., Tsakalidis, A., Zhou, Y., Wang, B., Liakata, M., Zubiaga, A., …Procter, R. (2015). WarwickDCS : from phrase-based to target-specific sentiment recognition. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) (657-663). https://doi.org/10.18653/v1/s15-2110

We present and evaluate several hybrid systems for sentiment identification for Twitter, both at the phrase and document (tweet) level. Our approach has been to use a novel combination of lexica, traditional NLP and deep learning features. We also an... Read More about WarwickDCS : from phrase-based to target-specific sentiment recognition.

Real-time timeline summarisation for high-impact events in Twitter (2016)
Presentation / Conference Contribution
Zhou, Y., Kanhabua, N., & Cristea, A. (2016). Real-time timeline summarisation for high-impact events in Twitter. In G. A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, & F. van Harmelen (Eds.), Proceedings of the 22nd European Conference on Artificial Intelligence, 29 August–2 September 2016, The Hague, The Netherlands (1158-1166). https://doi.org/10.3233/978-1-61499-672-9-1158

Twitter has become a valuable source of event-related information, namely, breaking news and local event reports. Due to its capability of transmitting information in real-time, Twitter is further exploited for timeline summarisation of high-impact e... Read More about Real-time timeline summarisation for high-impact events in Twitter.

Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum (2018)
Presentation / Conference Contribution
Tsakalidis, A., Aletras, N., Cristea, A., & Liakata, M. (2018). Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (367-376). https://doi.org/10.1145/3269206.3271783

Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising. Previous approaches mainly focus on predicting national general election... Read More about Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum.

How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers (2018)
Presentation / Conference Contribution
Cristea, A., Alshehri, M., Alamri, A., Kayama, M., Stewart, C., & Shi, L. (2018). How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Proceedings of the 27th International Conference on Information Systems Development (ISD2018), Education Track, Lund, Sweden, August 22-24, 2018

Data-intensive analysis of massive open online courses (MOOCs) is popular. Researchers have been proposing various parameters conducive to analysis and prediction of student behaviour and outcomes in MOOCs, as well as different methods to analyse and... Read More about How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers.

Zero-cost labelling with web feeds for weblog data extraction (2013)
Presentation / Conference Contribution
Gkotsis, G., Stepanyan, K., Cristea, A., & Joy, M. (2013). Zero-cost labelling with web feeds for weblog data extraction. In WWW '13 Companion : Proceedings of the 22nd international conference on World Wide Web companion (73-74)

Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia (2016)
Presentation / Conference Contribution
Zhou, Y., Demidova, E., & Cristea, A. (2016). Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia. In Proceedings of the 2016 ACM Symposium on Applied Computing : Artificial Intelligence and Agents, Distributed Systems, and Information Systems (750-757). https://doi.org/10.1145/2851613.2851858

In this paper we take an important step towards better understanding the existence and extent of entity-centric language-specific bias in multilingual Wikipedia, and any deviation from its targeted neutral point of view. We propose a methodology usin... Read More about Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia.

In-depth Exploration of Engagement Patterns in MOOCs (2018)
Presentation / Conference Contribution
Lei, S., & Cristea, A. (2018). In-depth Exploration of Engagement Patterns in MOOCs. In H. Hacid, W. Cellary, H. Wang, H. Paik, & R. Zhou (Eds.), Web information systems engineering - WISE 2018 : 19th International Conference, Dubai, United Arab Emirates, November 12-15, 2018. Proceedings. Part II (395-409). https://doi.org/10.1007/978-3-030-02925-8_28

With the advent of ‘big data’, various new methods have been proposed, to explore data in several domains. In the domain of learning (and e-learning, in particular), the outcomes lag somewhat behind. This is not unexpected, as e-learning has the addi... Read More about In-depth Exploration of Engagement Patterns in MOOCs.

Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems (2023)
Journal Article
Arnau-González, P., Serrano-Mamolar, A., Katsigiannis, S., Althobaiti, T., & Arevalillo-Herráez, M. (2023). Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems. IEEE Access, 11, 67030-67039. https://doi.org/10.1109/access.2023.3290478

Math Word Problem (MWP) solving, which involves solving math problems in natural language, is a prevalent approach employed by Intelligent Tutoring Systems (ITS) for teaching mathematics. However, one major drawback of ITS is the complexity of encodi... Read More about Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems.