Discovery and detection of novice Java programmer errors

Building models of students is a complex task, but it cannot be avoided because of the relevance of such knowledge to adaptive systems such as intelligent tutoring systems. Machine learning techniques have been applied to the task of student modeling, more so in building tutors for acquiring programming skill. It had been developed for various languages (Pascal, Prolog, Lisp, C++) and programming paradigms (procedural and declarative) but never for object-oriented programming in Java. Java Bugs builds a bug library automatically using discrepancies between a student and correct program. While other works analyze code snippets or UML diagrams, Java Bugs examines a complete Java program and identifies the most similar correct program to the student solution. It has to find the most similar correct program among a collection of correct solutions. Java Bugs uses a two-pass multi strategy conceptual clusterer to build local and global error hierarchies. A local error hierarchy is a tree of student misconceptions, while a summary of common, co-occurring misconceptions in local error hierarchies are stored as the global error hierarchy. Java Bugs uses not only similarity, but background knowledge and generalization of misconceptions to discover and detect student misconceptions. Experiments show that Java Bugs can detect the most similar correct program 97% of the time, and discover and detect 74.95% of misconceptions identified by the expert. Keywords: bug library, multi strategy learning, conceptual clustering, Java object-oriented programming

Laughter classification using 3D convolutional neural networks

Social signals express the attitude of human being in social situations. Laughter has been determined as an important social signal that can predict emotional information of people. It conveys different emotions such as happiness, surprise, fear, anger, and anxiety. Therefore, identifying and extracting emotions in the laughter is useful for estimating the emotional state of the user. Deep neural networks are replacing traditional methods because they perform more accurately. This paper presents work that detects the emotions in laughter by using audio features and running 3D Convolutional Neural Networks. The best rate of accuracy produced by 3D CNNs is 97.97%, which is higher than the results of our previous paper, which applied MLP and SVM on Iranian laughter dataset. © 2019 ACM

Towards context based affective computing

This is an introduction to the Second International Workshop on Context Based Affect Recognition CBAR 2013 Held in conjunction with Affective Computing and Intelligent Interaction 2-5 September 2013, Geneva, Switzerland. © 2013 IEEE

Comparing affect recognition in peaks and onset of laughter

Laughter is an important social signal that conveys different emotions like happiness, sadness, anger, fear, surprise, and disgust. Therefore, detecting emotions in the laughter is useful for estimating the emotional state of the user. This paper presents work that detects the emotions in Iranian laughter by using audio features and running four machine learning algorithms, namely, Sequential Minimal Optimization (SMO), Multilayer Perceptron (MLP), Logistic, and Radial Basis Function Network (RBFNetwork). We extracted features such as intensity (minimum, maximum, mean, and standard deviation), energy, power, first 3 formants, and the first thirteen Mel Frequency Cepstral Coefficients. Two datasets are used: one that contains segments of full laughter episodes and one that contains only laughter onsets. Results indicate that MLP algorithm produce the highest rate of accuracy which is 86.1372% for first dataset and 85.0123% for second dataset. Besides, using the combination of MFCC and prosodic features led to better results. This means that recognition of emotions is possible at the start of laughter, which is useful for real-time applications. © Springer International Publishing Switzerland 2016

Automatic construction of a bug library for object-oriented novice Java programmer errors

Machine learning techniques have been applied to the task of student modeling, more so in building tutors for acquiring programming skill. These were developed for various languages (Pascal, Prolog, Lisp, C++) and programming paradigms (procedural and declarative) but never for object-oriented programming in Java. JavaBugs builds a bug library automatically using discrepancies between a student and correct program. While other works analyze code snippets or UML diagrams to infer student knowledge of object-oriented design and programming, JavaBugs examines a complete Java program and identifies the most similar correct program to the student\u27s solution among a collection of correct solutions and builds trees of misconceptions using similarity measures and background knowledge. Experiments show that JavaBugs can detect the most similar correct program 97% of the time, and discover and detect 61.4% of student misconceptions identified by the expert. © 2008 Springer-Verlag Berlin Heidelberg

Recognizing student emotions using brainwaves and mouse behavior data

Brainwaves (EEG signals) and mouse behavior information are shown to be useful in predicting academic emotions, such as confidence, excitement, frustration and interest. Twenty five college students were asked to use the Aplusix math learning software while their brainwaves signals and mouse behavior (number of clicks, duration of each click, distance traveled by the mouse) were automatically being captured. It is shown that by combining the extracted features from EEG signals with data representing mouse click behavior, the accuracy in predicting academic emotions substantially increases compared to using only features extracted from EEG signals or just mouse behavior alone. Furthermore, experiments were conducted to assess the prediction accuracy of the system at points during the learning session where several of the extracted features significantly deviate in value from their mean. The experiments confirm that the prediction performance increases as the number of feature values that deviate significantly from the mean increases. Copyright © 2013, IGI Global

Exploring Micro-Entrepreneurs’ Trust on Customers in Social Commerce: Perspective from an Emerging Economy

With the recent surge of entrepreneurs engaging in online businesses using social media, micro-entrepreneurs are confronted with various challenges that impede their entrepreneurial growth. While successful e- commerce transactions require trust from both parties, extant literature have focused on trust from the customer\u27s perspective, neglecting the importance of entrepreneurs’ trust in the success of online transactions. This work investigates how micro-entrepreneurs assess their customers’ trustworthiness, including how they address trust issues arising from social commerce. Following the thematic analysis from the 30 in-depth interviews; the legitimacy of a customer, online community members’ support, social interaction and assurance emerged as prevailing concepts of trust in the conduct of social commerce among Filipino micro-entrepreneurs. This paper also indicated that these dimensions of trust led online entrepreneurs to develop new business practices. We conclude this paper by discussing the implications of our study and the limitations to provide future scholarly directions

Social Media as Enabler for ICT Inclusion to Achieve Active Ageing

Social media is a potential tool to promote the active engagement of elderly individuals in online communities. Technological advancement in developed countries has spurred interest to improve the quality of life of individuals as they age. This cannot be said in the developing countries like the Philippines, where the digital divide is apparent. This work investigates how technology use, particularly social media, can promote active ageing among Filipino elderly. The paper presents the results of a study conducted among 168 elderly volunteers living in an urban area located in a country experiencing first-level digital divide. Using the lens of social cognitive theory, this quantitative study is directed to examine the personal, environmental and behavioral factors influencing the continued use and non-use of social media among the elderly. Results show that majority of the elderly in the urban area in the country are experiencing first-level digital divide. However, the paper also discovered that some elderly social media users actively participate in photo and video sharing, social networks, internet forums, and product reviews and ratings. The findings suggest that the obvious predictors of active use/behavior of elderly on social media are influenced by age, highest educational attainment, and capacity to own and access technology. These facilitate social bonding with their strong-tie relationships. To narrow the elderly’s digital gap, a proactive stance needs to be taken to prepare technological and social structures to allow the next generation of the elderly to practice active ageing