Suchergebnisse

Experiential learning refers to learning from experience or learning by doing. Universities have explored various forms for implementing experiential learning such as apprenticeships, internships, cooperative education, practicums, service learning, job shadowing, fellowships and community activities. However, very little has been done in systematically trying to integrate experiential learning to the main stream academic curriculum. Over the last two years, at the authors' university, a new program titled UNI-X was launched to achieve this. Combining academic curriculum with experiential learning pedagogy, provides a challenging environment for students to use their disciplinary knowledge and skills to tackle real world problems and issues through inter-disciplinary approaches and activities. A course designated as UNI-X involves external partners from corporate, non-profit or government-sector organisations. The course requires the student to learn knowledge and skills during the classroom sessions and then to apply them in solving a real-world project proposed by the partners. The instructor along with the industry partner plays an active role in the project design, mentoring and assessment. In this paper contribution, the authors share their experience in designing a Data Warehousing and Business Analytics (DWBA) course to include experiential learning activities. The paper describes in detail the content, pedagogy, in-course project, challenges and lessons learned when introducing experiential learning into an existing course. Thus providing one pathway for Information Systems (IS) professors to adapt their analytics course to include experiential learning activities.

Online user comments contain valuable user opinions. Comments vary greatly in quality and detecting high quality comments is a subtask of opinion mining and summarization research. Finding attentive comments that provide some reasoning is highly valuable in understanding the user's opinion particularly in sociopolitical opinion mining and aids policy makers, social organizations or government sectors in decision making. In this paper we study the problem of detecting thoughtful comments. We empirically study various textual features, discourse relations and relevance features to predict thoughtful comments. We use logistic regression model and test on the datasets related to sociopolitical content. We found that the most useful features include the discourse relations and relevance features along with basic textual features to predict the comment quality in terms of thoughtfulness. In our experiments on two different datasets, we could achieve a prediction score of 79.37% and 73.47% in terms of F-measure on the two data sets, respectively.

Opinions are central to almost all human activities by influencing greatly the decision making process. In this thesis, we present the problems of mining issues, extracting entities and suggestive opinions towards the entities, detecting thoughtful comments, and extracting stances and ideological expressions from online comments in the sociopolitical domain. This study is essential for opinion mining applications that are beneficial for policy makers, government sectors and social organizations. Much work has been done to try to uncover consumer sentiments from online comments to help businesses improve their products and services. However, sociopolitical opinion mining poses new challenges due to complex topic and sentiment expressions. We first present the problem of issue extraction from sociopolitical comments for which we propose an unsupervised approach based on latent variable methods for identifying and extracting the issues in the comments, and linking comments to the issues in the associated article. We evaluate our approach on political speeches and associated comments from social media. In the sociopolitical domain, users express their sentiments on the entities such as individuals or organizations. These sentiments are not only in the form of positive and negative expressions, but also in the form of suggestive opinions towards the entities. We present a new problem of extracting the entities and associated suggestive opinions. We propose a two-stage approach based on conditional random fields (CRF) and clustering for extracting and normalizing the entities and the associated suggestive opinions from the users. A key feature of social media is that it enables anyone to freely express his/her opinions. As a result of the large amount of online comments, there is an urge for extracting opinions which are highly valuable. In terms of thoughtful comment extraction, we study the task of extracting valuable comments from social media. We propose a supervised approach based on natural language processing and linguistics techniques to identify and extract valuable comments in the sociopolitical domain from social media. Users take positions/stances and express opinions towards controversial sociopolitical issues. We present the problem of extracting the topics, stances, and ideological expressions of users from their comments on ideological debates related to sociopolitical domain. We propose an unsupervised approach based on latent variable methods and evaluate on Debatepedia for identifying and extracting the positional words and entities associated with the issues. In summary, this thesis identifies a number of key problems in mining sociopolitical comments and proposes appropriate solutions to these problems.

As with any other generational cohort,Generation Z students have their own unique characteristics that influencetheir approach to learning process. They are the future workforce and severalefforts are undertaken by Government and education institutes to consider thecharacteristics of Gen-Z in developing the curriculum and teaching environmentsuitable for these students. E-learning plays a key role in students learningprocess and has been widely adopted by many education institutions. Inparticular, videos play a major role in the learning process of Gen-Zstudents. The purpose of this paper isto focus the on requirements of Gen-Z students and to provide suggestions forhow to create a e-learning video environment for them. This study aids theeducation institutes in designing and developing e-learning environmentappropriate for Gen-Z students. For this project, we study the e-learningvideos environment, Echo 360, and the behavior of Gen-Z students to suggest theadditional features to improve the e-learning video environment. Around 76students participated in our study where they had to complete an e-learningsession and a qualitative survey to provide feedback about the session. Ourstudy shows that the students appreciate the video based learning due theflexibility, pace and independence. At the same time, they prefer onlineinteractions, and provided technology based suggestions for e-learningenvironment improvements.

With the growing popularity of opinion-rich resources on the Web, new opportunities and challenges arise and aid people in actively using such information to understand the opinions of others. Opinion mining process currently focuses on extracting the sentiments of the users on products, social, political and economical issues. In many instances, users not only express their sentiments but also contribute their ideas, requests and suggestions through comments. Such comments are useful for domain experts and are referred to as actionable content. Extracting actionable knowledge from online social media has attracted a growing interest from both academia and the industry. We define a new problem in this line which is extracting entity-actionable knowledge from the users' comments. The problem aims at extracting and normalizing the entity-action pairs. We propose a principled approach to solve this problem and detect exactly matched entities with 75.1% F-score and exactly matched actions with 76.43% F-score. We could achieve an average precision of 81.15% for entity-action normalization.

Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be generalized to include three components — users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an integrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1% for user party detection task.

Ageing population would cause profound problems and the impact is already being felt today in many developed countries such as Singapore. The main concern for the Government is to help the citizens with active ageing through home ownership and good healthcare. With Internet of Things (IoT) gaining traction globally, Singapore is set to take advantage of this technology and leverage it to extend its capabilities towards a graceful Ageing-In-Place for the elderly. This ties in nicely with the expertise of SHINE Seniors project by SMU-iCity Lab, which integrates IT with healthcare in ways that creates innovative IT health solutions that meet the needs of the elderlies. In this project, we study the problem of predicting potential Alzheimer conditions in the elderly through the behavioural analysis models developed from IoT sensors data. Our findings shows that IoT room sensors for location detection can enable us the capture the key three variables of elderly behaviour; excess active levels, sleeping patterns and repetitive actions. The three variables are useful in predicting the early warning signs of Alzheimer and we provide recommendations to care-givers based on the prediction analysis. We studied the task on 20 elderly living alone in the flats equipped with five sensors with the data spread over a period of 6 months.