Paul Salvador Inventado

Educational Data Mining and Learning Analytics

In recent years, two communities have grown around a joint interest on how big data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of educational data mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.

The TALA Empathic Space: Integrating Affect and Activity Recognition into a Smart Space

Advancement in ambient intelligence is driving the trend towards innovative interaction with computing systems. In this paper, we present our efforts towards the development of the ambient intelligent space TALA, which has the concept of empathy in cognitive science as its architectures backbone to guide its human-system interactions. We envision TALA to be capable of automatically identifying its occupant, modeling his/her affective states and activities, and providing empathic responses via changes in ambient settings. We present here the empirical results and analyses we obtained for the first two of this three-fold capability. We constructed face and voice datasets for identity and affect recognition and an activity dataset. Using a multimodal approach, specifically, applying a decision level fusion of independent face and voice models, we obtained accuracies of 88% and 79% for identity and affect recognition, respectively. For activity recognition, classification is 80% accurate even without employing any fusion technique.

Observatory: A Tool for Recording, Annotating and Reviewing Emotion-Related Data

Emotion is an important field of study shared by many disciplines such as psychology, language and computer science. Most studies in emotion require the collection and annotation of data, but many issues arise from this process. This research focused on the following issues: collecting data from multiple sources, providing flexibility in emotion labels for annotation, providing additional contextual information during annotation, and annotating non-observable emotion-related data. The Observatory software is introduced as a solution that addresses these issues. Using this single software, researchers can record, annotate and review emotion-related data.

BUILDING POLICIES FOR SUPPORTIVE FEEDBACK IN SELF-DIRECTED LEARNING SCENARIOS

Students often face difficulty in self-directed learning scenarios (e.g., studying, research) because they need to control many aspects of the learning session. They need to decide what to learn, how long to perform a learning task, when to shift to a different learning task and manage distractions apart from others. We observed from our previous research that self-reflection and self-evaluation helped students manage their own learning. However, majority of the students only evaluated one or two major aspects of the learning session that they think needed to be changed or improved (e.g., need to spend less time in nonlearning related activities, need to focus on only one learning task at a time). If students would look further into their learning session, they would discover more behaviors that also need to be re-evaluated. In this paper we discussed reinforcement learning-based methods for discovering good learning behavior which can be used by future systems to suggest to students possible ways to improve their behavior.

Identification of Effective Learning Behaviors

Self-regulated learners have been shown to learn more effectively. However, it is not easy to become self-regulated because learners have to be capable of observing and evaluating their thoughts, actions and behaviors while learning. In this work, we used Q-learning to reveal the effectiveness or ineffectiveness of a learning behavior that carries over learning episodes. We also showed different types of effective learning behavior discovered and how they were differentiated. Providing learners with knowledge about learning behavior effectiveness can help them observe how strategy selection affects their performance and will help them select more appropriate strategies in succeeding learning episodes for better future performance.

Sidekick Retrospect: A Self-regulation Tool for Unsupervised Learning Environments

Self-regulation is an important skill for students to possess. It allows them to learn more effectively and it has been shown to cause better learning gains. Self-regulation is not an easy task especially for poor learners. This is the motivation behind researches that use computer-based learning environments to promote self-regulation through embedded tools that help students keep track of their self-regulation processes. Although these researches have shown promising results, they focus on self-regulation processes inside controlled learning environments. Not much research has been done on learning in unsupervised learning environments where students learn on their own, introducing additional challenges. In this research, we developed software to help students perform self-regulation in this setting. Results showed that the software was able to help students set goals, monitor their activities and evaluate their learning behavior. Students who used the software reported that it made them more aware of the activities they did when they were learning and it also helped them identify what to do in order to improve their learning behavior in succeeding learning sessions.

Categorizing and Comparing Behaviors of Students Engaged in Self-initiated Learning Online

The current generation is much accustomed to new technologies which enable them to perform many activities online. More importantly, these technologies have been used by students even for learning. In this research we focused on student initiated learning online. Because students have control over their own learning, they are not bounded by a syllabus or a specific learning task. Apart from learning related activities however, it is possible for them to engage in non-learning related activities. From the data gathered, the k-Means algorithm was used to discover five behaviors exhibited by students as they learned online relative to how they transitioned between viewing learning and non-learning related websites. Since emotion was previously reported to have an effect on how students learned online, differences in emotion transitions for each of the online learning behaviors were also observed. The analysis of these transitions provided possible reasons for why students exhibited these behaviors. Possible interventions were then suggested which can be used for supporting students as they learn online. Systems can later be developed to utilize the developed model for predicting the type of behavior exhibited by a student and provide appropriate support mechanisms for their learning.