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Automated question generation is a task to generate questions from structured or unstructured data. The increasing popularity of online learning in recent years has given momentum to automated question generation in education field for facilitating learning process, learning material retrieval, and computer-based testing. This paper report on the development of question generation framework based on key-phrase method for online learning with a constraint that the generated questions should comply with the learning outcomes and skills from Bloom's Taxonomy. The proposed method was tested using learning materials of Software Engineering course for undergraduate level written in Bahasa Indonesia obtained from Bina Nusantara's (Binus's) Online Learning repository. Using one-semester lecture material, this study generated 92,608 essay-type questions from 6-level Bloom's Taxonomy which were further sampled randomly to obtain 120 question samples for method evaluation. Performance evaluation using average Bilingual Evaluation Understudy (BLEU) involving five independent reviewers toward samples of these questions achieved 0.921 and 0.6 Cohen's Kappa. The relevance of Bloom's Taxonomy level of the generated questions was evaluated by means of classification model with 0.99 accuracy. The results indicate that not only are the generated questions well understood and agreed by the reviewers, they are also relevant to the expected Bloom's Taxonomy level there for the questions can be delivered to students in the respected course delivery and evaluation.

The recommender system has gained research attention from education research communities mainly due to two main reasons: increasing needs for personalized learning and big data availability in the education sector. This paper presents a hybrid user-collaborative, rule-based filtering recommendation system for education context. User profiles are represented by learning outcome scores and contextual information. The user-collaborative filtering method is used for predicting the targeted student's learning outcome of a particular course. The predicted learning outcome combined with a set of decision rules are used for recommending some relevant link of learning materials to the targeted student. The initial contextual information which is assessed during the online program entrance test makes it possible for the proposed recommneder system to give automated recommendations to new students. The proposed method was tested using student learning outcome records from BINUS Online repository data. The results of performance evaluation of both recommender system with contextual information which achieves 458.22 MSE and the recommender system without contextual information which achieves 413.19 MSE are not significantly different. However, unlike the latter recommender system, the former recommender system has an advantage mainly that it can be used to give recommendation to the targeted students since their early program stage.

There are lots of information and knowledge can be extracted from a discussion forum. Despite a discussion is opened by submitting a thread as the topic of discussion, however, the discussion may open out to different topics. This paper aims to present a model to find out a topic of discussion through latent semantic approach, named Topics Finding Model (TFM). The model proposes a complete step to reveal the topic of discussion from a thread in a discussion forum, consisting of the pre-processing text document, corpus classification and finding a topic. The model can be applied in various discussion forums and various languages with a few adjustments, such as stop-word removal list and stemming algorithm. The data were obtained from discussion forum in a learning management system. The data consist of 1050 posts divided into three different course subjects: information systems, management, and character building. The reason for using several course subjects is to observe consistency of the model. F-measure was used to measure the effectiveness of the model, and the results showed that the TFM was consistent and effective to reveal the topic of discussion, with a good precision. However, the recall can still be increased in a further study.

This paper presents our work on the development of Path Planning Strategy and Navigation by using ANFIS(Adaptive Neuro-Fuzzy Inference System)controller for a vision-based service robot. The robot will deliver a cup to a recognized customer and a black line as the guiding track for navigating a robot with a single camera. The contribution of this research includes a proposed architecture of ANFIS controller for vision-based service robot integrated with improved face recognition system using PCA, and the algorithm for moving obstacle avoidance. We also propose a path planning algorithm based on Dijkstra's algorithm to obtain the shortest path for robot to move from the starting point to the destination. In order to avoid moving obstacles, we have proposed an algorithm using binaural ultrasonic sensors. The service robot called Srikandi is also equipped with 4 DOF arm and a framework of face recognition system. The proposed path planning strategy and navigation was tested empirically and proved effective.