## 3 Materials and Methods
### Analytics on Lifleogging Images: Fitting into Language Learning Research  
In this study's scope, we aimed to understand how analytics on lifleogging images or image understanding using AI-models for contextualization fits into the computer-assisted language learning discipline. To do so, we use an example of a learning scenario, for instance, in a learning situation where a learner knows words such as _breakfast_ and _sandwich_. Afterward, the same learner captures a picture in a new learning situation and uploads it into the IUEcosystem' server. The system will analyze the visual contents of the image and produces smartly-generated learning contexts (which can be exampled as, 1. _A plate of snacks_, 2. _Combination of sandwich_, _spoon_, _and_ soup_, 3. _A breakfast plate with sandwich_, _plate_, _and_ _spoon_) by which the learner's prior vocabulary knowledge can be triggered. Those words can be mapped with the smartly-generated learning contexts represented by the system. In addition, the learner could learn new words such as _spoon_, _plate_, and _snacks_. In this work, we aimed to develop the IUEcosystem in a way that will accept the pictures that are uploaded by the registered  
Figure 4: The method for time-based notification.  
Figure 3: The user interface to collect feedback.  
users. Then, the system will analyze the visual contents of those pictures. Furthermore, based on those visual contents, the system will produce smartly-generated learning contexts.  
By doing so, some educational advantages could be achieved, such as:  
1. The smartly-generated learning contexts will in mapping a learner's prior vocabulary knowledge with new knowledge so that they can review and recall the previously acquired vocabulary [29];
2. The smartly-generated learning contexts will help a learner to memorize new vocabularies;
3. The smartly-generated learning contexts may help in acquiring multiple vocabularies from a single learning experience [29];
4. The smartly-generated learning contexts may provide the visual mnemonics that will work as the visual cues to highlight that particular word (i.e., the target word) in a specific learning context.  
In Figure 5, we present the learning design of the IUEcosystem. The learning design of the IUEcosystem is based on three technologies, namely a Learning Context Representation Model (LCRM), a Learning Context Analytics (LCA), and a Picture-assisted Quiz System (PQS). The first technology, the LCRM, is used to analyze the scenes of the lifelogging images. The LCRM generates smartly-generated learning contexts. The second technology, the LCA, analyzes the smartly-generated learning contexts for producing a bag of words that contains many new vocabularies. We assume this will enhance the learning scope and increase learning curiosity. The third technology, the PQS, is used to generate picture-based quizzes so that the learner can review their knowledge after a certain period.  
Although computer vision and natural language learning research have advanced, producing meaningful learning contexts is not easy. Besides, connecting learning analytics with computer vision and natural language processing is quite a challenging task. Leveraging analytics on lifelogging images using image captioning technology, we aimed to build an environment where learning context and multiple vocabulary acquisition and reflection of prior knowledge to learn new knowledge are supported. To our best knowledge, no recognized research in computer-assisted language learning literature has used scene analysis or image analytics for understanding learning contexts for foreign vocabulary learners. Therefore, we believe this paper will bring newness and opens the scopes to new research.