Learner ontology is used to capture knowledge about the learner, thus enabling the system to discover, adapt, and deliver the most relevant learning resources based on their needs, background, and preferences.
It is necessary to further analyze the learner products using the data collected by the system to get better view of learner's problem posing process in order to capture learner's understanding of math and science concepts (Birch and Beal 2008).
In such learning environment, the context of the learner matters, and the system should be able to capture the learner's profile, learner's context, task at hand, and the environment in which learning occurs.
The LAMP is a five-point Likert-scale questionnaire designed to capture learners' motivations and attitudes towards English pronunciation practice and learning.
Previous research has tended to capture learners' SRL awareness using questionnaires or observations (e.g., Pintrich and DeGroot 1990; Zimmerman 2008; Cho and Jonassen 2009; Jansen, van Leeuwen, Janssen, Kester, and Kalz 2016).
For example, Moreno-Ger et al. (2007) evaluated the learner's level of knowledge as part of learner modelling through a questionnaire and some basic tests to capture the learner's initial level of knowledge.
Meanwhile, for the incorrect attempt, the first text-based mediation (the most implicit one) was offered to assist the learner with a new timer which was responsible to capture the learner's response latency in the second attempt.
The knowledge structure of an online instructional/learning system can serve as a guide that directs an inexperienced learner to appropriately capture knowledge.
Along each presentation of mediation with any quality, the timer played its role to capture the learners' response latency.
As noted by Poehner et al. (2014), the aim of C-DA is not to increase the students' scores but to capture the learner's ZPD, i.e., to attempt to diagnose the learner's actual and potential level of development.
