Sébastien Louvigné

Utilizing Social Media for Goal Setting Based on Observational Learning

Goal-Setting enhances learning by providing a sense of direction and purpose. Often only a few goals are suggested, as a result many learners fail to find the goals that they can relate to. To address this problem, we propose to extract a large number and variety of goals from social media. Learners can then observe goal-based messages from others and adopt the ones they find useful. Conceptually, this approach could be considered a combination of Goal-Setting and Observational Learning. To provide a practical implementation, we automate this process by (1) retrieving a large number of messages from Twitter, (2) classifying which ones contain goals, (3) determining what those goals are.

Goal-Based Messages Recommendation Utilizing Latent Dirichlet Allocation

Observing various learning goals from peers allows learners to specify new objectives and sub-goals to improve their personal experience. Setting goals for learning enhances motivation and performance. However an unrelated goal might lead to poor outcome. Hence learners have divergent objectives for a same learning experience. Latent Dirichlet Allocation (LDA) is a model considering documents as a mixture of topics. This study then proposed a recommendation model based on LDA, able to determine distinct categories of goals within a single dataset. Results focused on a dataset of 10 learning subjects and over 16,000 goal-based Twitter messages. It showed (1) different goal categories and (2) the correlation between the LDA parameter for the number of topics and the type of subject. Evaluations of goal attributes also showed an increase of goal specificity, commitment and self-confidence after observing different types of goals from peers.

SNS Messages Recommendation for Learning Motivation

Setting goals for learning enhances motivation and performance. This research shows that observing learning goals from peers on social networks allows learners to specify new learning purposes and to enhance the perception of their own expertise. This study consists of: 1) a model recommending goal-based messages from peers with diverse textual contents (i.e. purpose) for a same goal (e.g. mastering English), and 2) a Web-based implementation using an LDA (Latent Dirichlet Allocation) model, known as a highly accurate text latent topic model. The experiment was conducted by university students who expressed and evaluated their goals before observing similar/diverse messages from other peers. Results showed that observing the diversity of peers’ learning purposes is an important factor positively affecting intrinsic motivational attributes such as goal specificity and confidence to achieve the goal.

Utilizing Social Media for Observational Goal Setting

Goal-Setting enhances learning by providing a sense of direction and purpose. Often only a few goals are suggested, as a result many learners fail to find the goals that they can relate to. To address this problem, we propose to extract a large number and variety of goals from social media. Learners can then observe goal-based messages from others and adopt the ones they find useful. Conceptually, proposed approach could be considered a combination of Goal-Setting and Observational Learning. To provide a practical implementation, we automate this process by (1) retrieving a large number of messages from Twitter, (2) classifying which of the messages contain goals, (3) determining what those goals are.