We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submitted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity prediction), ranking highest out of 36 systems on the restaurant test set and joint highest out of 32 systems on the laptop test set.

In this work, we propose a new methodology to profile individual students of computer science based on their programming design using a technique called embeddings. We investigate different approaches to analyze user source code submissions in the Python language. We compare the performances of different source code vectorization techniques to predict the correctness of a code submission. In addition, we propose a new mechanism to represent students based on their code submissions for a given set of laboratory tasks on a particular course. This way, we can make deeper recommendations for programming solutions and pathways to support student learning and progression in computer programming modules effectively at a Higher Education Institution. Recent work using Deep Learning tends to work better when more and more data is provided. However, in Learning Analytics, the number of students in a course is an unavoidable limit. Thus we cannot simply generate more data as is done in other domains such as FinTech or Social Network Analysis. Our findings indicate there is a need to learn and develop better mechanisms to extract and learn effective data features from students so as to analyze the students' progression and performance effectively.

Much research in information retrieval (IR) focuses on optimization of the rank of relevant retrieval results for single shot ad hoc IR tasks. Relatively little research has been carried out on user engagement to support more complex search tasks. We seek to improve user engagement for IR tasks by providing richer representation of retrieved information. It is our expectation that this strategy will promote implicit learning within search activities. Specifically, we plan to explore methods of finding semantic concepts within retrieved documents, with the objective of creating improved document surrogates. Further, we would like to study search effectiveness in terms of different facets such as the user's search experience, satisfaction, engagement and learning. We intend to investigate this in an experimental study, where our richer document representations are compared with the traditional document surrogates for the same user queries.

We describe the work carried out by the DCU team on the Semantic Textual Similarity task at SemEval-2015. We learn a regression model to predict a semantic similarity score between a sentence pair. Our system exploits distributional semantics in combination with tried-and-tested features from previous tasks in order to compute sentence similarity. Our team submitted 3 runs for each of the five English test sets. For two of the test sets, belief and headlines, our best system ranked second and fourth out of the 73 submitted systems. Our best submission averaged over all test sets ranked 26 out of the 73 systems.

We describe an initial study into the identification of important and useful information units within documents retrieved by an information retrieval system in response to a user query created in response to an underlying information need. This study is part of a larger investigation of the exploitation of useful and important units from retrieved documents to generate rich document surrogates to improve user search experience. We report three user studies using a crowdsourcing platform, where participants were first asked to read an information need and contents of a relevant document and then to perform actions depending on the type of study: i) write important information units (WIIU), ii) highlight important information units (HIIU) and iii) assess importance of already highlighted information units (AIHIU). Further, we discuss a novel mechanism for measuring similarities between content annotations. We find majority agreement of about 0.489 and pairwise agreement of 0.340 among users annotation in the AIHIU study, and average cosine similarity of 0.50 and 0.57 between participant annotations and documents in the WIIU and HIIU studies respectively.