Trends in student behavior in online courses

Learning management systems provide an easy and effective means of access to learning materials. Students’ access to course material is logged and the amount of interaction is assumed to be a measure of student engagement within the course. In previous research, typically frequencies of student activities have been used, but this disregards any temporal information. Here, we analyze the amount of student activity over time during courses. Based on activity data over 11 online courses, we cluster students who show similar behavior over time. This results in three different groups: a large group of students who are mostly inactive; another group of students who are very active throughout the course; and a group of students who start out being active, but their activity diminishes throughout the course. These groups of students show different performance. Overall, more active students yield better results. In addition to these general trends, we identified courses in which alternative trends can be found, such as a group of students who become more active during the course. This shows that student behavior is more complex than can be identified from an individual course and more research into patterns of learning activities in multiple courses is essential

MEANING OF SENTIMENT ANALYSIS FOR COMPANIES

People often ask others for product advice. Once, word-of-mouth (WOM) was, due to practical limitations, shared locally.  Nowadays, WOM is shared online (eWOM), which has a much larger reach.  As eWOM is publicly accessible (unlike WOM), it can be used as information on brand attitude.  eWOM can be aggregated and assessed using sentiment analysis (identifying positive/negative messages). The assumption is that sentiment analysis illustrates people's brand perception. We investigate the relationship between sentiment analysis and brand perception.  We collected tweets with sentiment information of eight brands in Indonesia using Twitter's built-in sentiment analysis over a week.  Using these tweets, aggregated sentiment scores were computed. The scores were correlated with brand perception collected using questionnaires. 206 participants attributed scores to seven properties: Complaint handling, Design, Friendliness, Information, Marketing, Service, and Overall score.  Either insignificant or correlations close to zero were found, so online sentiment does not correspond to offline brand perception.Keywords: word-of-mouth, artificial intelligence in business, sentiment analysi

Understanding the Keystroke Log:The Effect of Writing Task on Keystroke Features

Keystroke logging is used to automatically record writers' unfolding typing process and to get insight into moments when they struggle composing text. However, it is not clear which and how features from the keystroke log map to higher-level cognitive processes, such as planning and revision. This study aims to investigate the sensitivity of requently used keystroke features across tasks with different cognitive demands. Two keystroke datasets were analyzed: one consisting of a copy task and an email writing task, and one with a larger difference in cognitive demand: a copy task and an academic summary task. The differences across tasks were modeled using Bayesian linear mixed effects models. Posterior distributions were used to compare the strength and direction of the task effects across features and datasets. The results showed that the average of all interkeystroke intervals were found to be stable across tasks. Features related to the time between words and (sub)sentences only differed between the copy and the academic task. Lastly, keystroke features related to the number of words, revisions, and total time, differed across tasks in both datasets. To conclude, our results indicate that the latter features are related to cognitive load or task complexity. In addition, our research shows that keystroke features are sensitive to small differences in the writing tasks at hand

Can virtual reality act as an affective machine? The wild animal embodiment experience and the importance of appearance

In view of the growing urgency to protect wildlife, the general goal of our research is to develop an immersive virtual experience where users can step into the ‘shoes’ of wild animals. The specific objective of this research is to explore the possibility of creating a strong emotional connection experience with a virtual animal body. In a game setting, users explore a simulated natural habitat of the animal. At the end of the game, users experience a distress event during which they become the target of an illegal animal hunter. The users receive physical feedback through haptic virtual reality suits (vibrating motors) that mimic the sensation of feeling pain of a hunter's shot. We compare the perceived pain, empathy, immersion, and embodiment experience evoked through a game character with a natural body (beaver), with an artificial body (robot beaver), and an amorphous body. The results of this investigation show a significant effect of game character appearance and perceived pain during the distress event. Moreover, we find a significant effect of game character appearance on immersion. These results suggest that the design of the game character appearance can influence users’ emotional connectedness to the character and the game experience

Problems with evaluation of unsupervised empirical grammatical inference systems

Abstract. Empirical grammatical inference systems are practical systems that learn structure from sequences, in contrast to theoretical grammatical inference systems, which prove learnability of certain classes of grammars. All current empirical grammatical inference evaluation methods are problematic, i.e. dependency on language experts, appropriateness and quality of an underlying grammar of the data, and influence of the parameters of the evaluation metrics. Here, we propose a modification of an evaluation method to reduce the ambiguity of results