The use of natural dialog has great significance in the design of interactive tutoring systems. The nature of student queries can be confined to a small set of templates based on the task domain. This paper describes the development of a chatbot for medical students, that is based on the open source AIML based Chatterbean. We deploy the widely available Unified Medical Language System (UMLS) as the domain knowledge source for generating responses to queries. The AIML based chatbot is customized to convert natural language queries into relevant SQL queries. The SQL queries are run against the knowledge base and results returned to the user in natural dialog. Student survey was carried out to identify various queries posed by students. The chatbot was designed to address common template queries. Knowledge inference techniques were applied to generate responses for queries for which knowledge was not explicitly encoded. Query responses were rated by three experts on a 1-5 point likert scale, who agreed among themselves with Pearson Correlation Coefficient of 0.54 and p < 0.05. The overall average rating assigned by experts was 3.4.
Creating a learning environment in which students learn more effectively remains the great challenge from decades; different approaches are proposed, for example, Intelligent Tutoring Systems, Question Answering System and chatbot. All these approaches used natural language to achieve that goal. A comparison of these systems viz-a-viz student learning outcome and behavior is of eminent importance. To achieve this goal a chatbot system with knowledge base for Object-Oriented Programming Languages is developed and deployed. Case study was made to assess and evaluate the chatbot system for student learning methodology. Learning outcomes and Memory retention have been measured for the developed system. Comparisons were made between the results obtained using Google search engine and our chatbot system. The results indicate that learning through Chabot have a significant impact on memory retention and Learning outcomes of the students.
Processing of social media text like tweets is challenging for traditional Natural Language Processing (NLP) tools developed for well-edited text due to the noisy nature of such text. However, demand for tools and resources to correctly process such noisy text has increased in recent years due to the usefulness of such text in various applications. Literature reports various efforts made to develop tools and resources to process such noisy text for various languages, notably, part-of-speech (POS) tagging, an NLP task having a direct effect on the performance of other successive text processing activities. Still, no such attempt has been made to develop a POS tagger for Urdu social media content. Thus, the focus of this paper is on POS tagging of Urdu tweets. We introduce a new tagset for POS-tagging of Urdu tweets along with the POS-tagged Urdu tweets corpus. We also investigated bootstrapping as a potential solution for overcoming the shortage of manually annotated data and present a supervised POS tagger with an accuracy of 93.8% precision, 92.9% recall and 93.3% F-measure.
While problem-based learning has become widely popular for imparting clinical reasoning skills, the dynamics of medical PBL require close attention to a small group of students, placing a burden on medical faculty, whose time is over taxed. Intelligent tutoring systems (ITSs) offer an attractive means to increase the amount of facilitated PBL training the students receive. But typical intelligent tutoring system architectures make use of a domain model that provides a limited set of approved solutions to problems presented to students. Student solutions that do not match the approved ones, but are otherwise partially correct, receive little acknowledgement as feedback, stifling broader reasoning. Allowing students to creatively explore the space of possible solutions is exactly one of the attractive features of PBL. This paper provides an alternative to the traditional ITS architecture by using a hint generation strategy that leverages a domain ontology to provide effective feedback. The concept hierarchy and co-occurrence between concepts in the domain ontology are drawn upon to ascertain partial correctness of a solution and guide student reasoning towards a correct solution. We describe the strategy incorporated in METEOR, a tutoring system for medical PBL, wherein the widely available UMLS is deployed and represented as the domain ontology. Evaluation of expert agreement with system generated hints on a 5-point likert scale resulted in an average score of 4.44 (Spearman's ρ=0.80, p<0.01). Hints containing partial correctness feedback scored significantly higher than those without it (Mann Whitney, p<0.001). Hints produced by a human expert received an average score of 4.2 (Spearman's ρ=0.80, p<0.01).
Serious Games (SG) provide a comfortable learning environment and are productive for various disciplines ranging from Science, Technology, Engineering, and Mathematics (STEM) to computer programming. The Object Oriented (OO) paradigm includes objects related to real life, and is considered a natural domain that can be worked with. Nonetheless, mapping those real-life objects with basic Object-Oriented Programming (OOP) concepts becomes a challenge for students to understand. Therefore, this study is concerned with designing and developing an SG prototype to overcome students’ difficulties and misconceptions in learning OOP and achieving positive learning outcomes. An experimental evaluation was carried out to show the difference between the experimental group students’ performance, who interact with the developed game, and students of the control group, who learn via the traditional instructional method. The experimental evaluations’ main finding is that the experimental group’s performance is better than the control group. The experimental group’s Normalized Learning Gain (NLG) is significantly higher than the control group (p< 0.005, paired t-test). The evaluation study results show that the developed prototype’s perceived motivation on the Instructional Materials Motivation Survey (IMMS) 5-point Likert scale resulted in the highest mean score for attention (3.87) followed by relevance (3.66) subcategories. The results of this study show that the developed SG prototype is an effective tool in education, which improves learning outcomes and it has the potential to motivate students to learn OOP.
Recent years have seen an increased interest in Data mining related to terrorism. Large volumes of terrorism records can be analyzed efficiently using data mining techniques to get solutions for crime investigation by law enforcement agencies. On 2014 edition of Global Terrorism Index (GTI, 2015), which systematically rank and compares 162 countries to the impact of terrorism, Pakistan was ranked as the third most affected country. The area of predicting terrorist incidents in the perspective of Pakistan is not adequately explored by the data mining research community which assert a serious concern. This study is focused on analyzing Incident data set from Global Terrorism Database (GTD) specific to Pakistan from year 1970 to 2014 by using predictive modeling. Prediction of future terrorist attacks according to City, Attack type, Target type, Claim mode, Weapon type and Motive of attack through classification techniques will facilitates the decision making process by security organizations as to learn from the previous stored attack information and then rate the targeted sectors/ areas of Pakistan accordingly for security measures.
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