Computational intelligence approaches for student/tutor modelling: a review

The intelligent tutoring system (ITS) is an educational software system that provides personalized and adaptive tutoring to students based on their needs, profiles and preferences. The tutor model and student model are two dependent components of any ITS system. The goal of any ITS system is to help the students to achieve maximum learning gain and improve their engagements to the systems by capturing the student's interests through the system's adaptive behavior. In other words an ITS system is always developed with the aim of providing an immediate and efficient solution to student's learning problems. In recent years a lot of work has been devoted to improving student and tutor models in order enhance the teaching and learning activities within the ITS systems. The aim of this paper is to investigate the most recent state of art in the development of these two vital components of the intelligent tutoring systems

Proposal for ontology based approach to fuzzy student model design

Intelligent tutoring system (ITS) is a software system designed using artificial intelligent techniques (comprising of Fuzzy Logic, Neural-Networks, Bayesian networks, Ontology, Genetic Algorithms and Software Agents) to provide an adaptive and personalized tutoring suitable to each individual student based on his/her profile or characteristics. In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student's learning behaviour with the aim of improving the learning path and increase the system's adaptability. The use of fuzzy logic in this context is to enable the computational analysis of the student's characteristics and learning behaviours in order to handle the uncertainty issues related to the student model design. Ontology is a vital tool for managing knowledge in a particular domain and is one of the recent techniques used to design the representation of student's cognitive state

A fuzzy logic approach to manage uncertainty and improve the prediction accuracy in student model design

The intelligent tutoring systems (ITSs) are special classes of e-learning systems developed using artificial intelligent (AI) techniques to provide adaptive and personalized tutoring based on the individuality of each student. For an intelligent tutoring system to provide an interactive and adaptive assistance to students, it is important that the system knows something about the current knowledge state of each student and what learning goal he/she is trying to achieve. In other words, the ITS needs to perform two important tasks, to investigate and find out what knowledge the student has and at the same time make a plan to identify what learning objective the student intends to achieve at the end of a learning session. Both of these processes are modeling tasks that involve high level of uncertainty especially in situations where students are made to follow different reasoning paths and are not allowed to express the outcome of those reasoning in an explicit manner. The main goal of this paper is to employ the use Fuzzy logic technique as an effective and sound computational intelligence formalism to handle reasoning under uncertainty which is one major issue of great concern in student model design

Prevalence of Mosquitoes in Gidan Yunfa Community of Usmanu Danfodiyo University, Sokoto, Nigeria

Different species of mosquito serves as a vector for transmitting malaria. Malaria is still a serious public health problem in Nigeria. Knowledge of the mosquito species, their diversity, and their composition would help immensely toward proper implementation of the different control strategies. This study was carried out to determine the prevalence of mosquitoes and feeding or biting period in Gidan Yunfa community of Usmanu Danfodiyo University, Sokoto, Nigeria. The Larvae and Pupae were collected from breeding sites. Adult mosquitoes were sampled using CDC light traps (situated indoor and outdoor) and Pyrethrum Spray Catch methods. Mosquitoes were identified morphologically. A total of 6,410 adult mosquitoes with 2,142 (33.42 %) obtained from CDC light traps and 4,268 (66.58%) from the larval collections were identified belonging to 3 genera Aedes, Anopheles, and Culex. A maximum number of mosquitoes were caught with CDC traps. The abundance of the different genera varied significantly (P<0.05) with Anopheles having the highest occurrence (54.75%) followed by Culex mosquitoes with 40.42%. Aedes has the least abundance with 8.05%. The indoor and outdoor feeding habits of the different species varied significantly (P<0.05). Nature of the houses and tethering of animal in residential houses and abundance of breeding places may explain the reason behind the higher prevalence of the mosquito in this community

Germinated brown rice and its bioactives modulate the activity of uterine cells in oophorectomised rats as evidenced by gross cytohistological and immunohistochemical changes

BACKGROUND: Germinated brown rice (GBR) is gaining momentum in the area of biomedical research due to its increased use as a nutraceutical for the management of diseases. The effect of GBR on the reproductive organs of oophorectomised rats was studied using the gross, cytological, histological and immunohistochemical changes, with the aim of reducing atrophy and dryness of the genital organs in menopause. METHODS: Experimental rats were divided into eight groups of six rats per group. Groups 1, 2 and 3 (sham-operated (SH), oophorectomised without treatment (OVX) and oophorectomised treated with 0.2 mg/kg oestrogen, respectively) served as the controls. The groups 4,5,6,7 and 8 were treated with 20 mg/kg Remifemin, 200 mg/kg of GBR, ASG, oryzanol and GABA, respectively. All treatments were administered orally, once daily for 8 weeks. Vaginal smear cytology was done at the 7th week on all the rats. The weight and dimensions of the uterus and vagina were determined after sacrifice of the rats. Uterine and vaginal tissues were taken for histology and Immunohistochemical examinations. RESULTS: GBR and its bioactives treated groups significantly increased the weight and length of both the uterus and the vagina when compared to Oophorectomised non-treated group (OVX-non-treated) (p < 0.05). Significant changes were observed in the ratio of cornified epithelial cells and number of leucocytes in the vaginal cytology between the oophorectomised non-treated and treated groups. There was also an increase in the luminal and glandular epithelial cells activity in the treated compared with the untreated groups histologically. Immunohistochemical staining showed specific proliferating cell nuclear antigen (PCNA) in the luminal and glandular epithelium of the treated groups, which was absent in the OVX-non-treated group. GBR improved the length and weight of the uterus and also increased the number of glandular and luminal cells epithelia of the vagina. CONCLUSION: GBR and its bioactives could be a potential alternative in improving reproductive system atrophy, dryness and discomfort during menopause

Artificial Intelligence Approaches in Student Modeling: Half Decade Review (2010-2015)

Intelligent Tutoring Systems (ITSs) are special classes of E-learning systems designed using Artificial Intelligence (AI) approaches to provide adaptive and personalized tutoring based on the individuality of students. The student model is an important component of an ITS that provides the base for this personalization. During the course of interaction between student and the ITS, the system observe student’s actions and other behavioral properties, create a quantitative representation of these student’s attributes called a student model

Laboratory-confirmed hospital-acquired infections:An analysis of a hospital's surveillance data in Nigeria

Objective: Hospital-acquired infections (HAI) are a global problem and a major public health concern in hospitals throughout the world. Quantification of HAI is needed in developing countries; hence we describe the results of a 2-year surveillance data in a tertiary hospital in Nigeria. Methodology: This study is a 2-year review using secondary data collected at a tertiary referral center in northwestern Nigeria. The data was collected using surveillance forms modeled based on the Centre for Disease Control (CDC) protocol. Descriptive statistics were used to present results as frequencies and percentages. Result: 518 patients developed HAI out of 8216 patients giving an overall prevalence of 6.3%. The mean age of the patients was 35.98 years (±15.92). Males constituted 281 (54.2%). UTI 223 (43.1%) was the most prevalent HAI. Overall, E. coli 207 (40.0%) was the most frequent isolates followed by P. aerugenosa 80 (15.4%). There was a high prevalence of cloxacillin resistant S. aureus (67.9%) and gram-negative rods resistant to third-generation cephalosporins. Trimethoprim-sulfamethoxazole resistance across the board was more than 90%. Conclusion: There is a high burden of HAI especially UTI in our hospital with resistance to commonly used antibiotics documented. Keywords: Public health, Infectious diseas

Fuzzy membership function and inference-based model for predicting student's knowledge performance

Intelligent Tutoring Systems (ITSs) are special classes of E-learning systems designed to provide adaptive and personalized tutoring based on the individuality of students. The student model is an important component of an ITS that provides the base for this personalization. During the course of interaction between student and the system, a quantitative representation of the actual student‟s characteristics such as the student‟s knowledge state is created based on the observations and predictions the ITS made on each student. The student‟s knowledge is one of the most dynamic characteristic of the student; so dynamic like a moving target. However, modeling student‟s knowledge and diagnosis are complex processes that are characterized by uncertainty and imprecision issues that affect the prediction of the student model. Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory. Its methodology provides a definitive solution to problems of information that may be construed as uncertain or imprecise. A major gap in the existing approach is the absence of an important component of a fuzzy logic controller, the membership function graph which is vital for the management of uncertainty and imprecision in student's knowledge modeling. Moreover, the prediction accuracies of 36% and 90% achieved by the existing models is seen as another limitations in their performance. This study propose novel fuzzy based membership functions and inference mechanisms to design a fuzzy logic based control process which aimed at modeling student's knowledge performance and further diagnosis. Successful design of this two vital fuzzy logic engines will respectively enables the realization of more accurate fuzzy student models and the necessary diagnosis on them. An approximate student model based on fuzzy membership function approach enables making accurate predictions about the state of student's knowledge. The main idea of proposing a novel fuzzy inference process is to provides the necessary diagnosis on the propose fuzzy student model. The inference process take outputs of a fuzzification process with membership functions as input variables for a mechanism which is defined by fuzzy If-Then rules and logical operators, and then reaches the output space that produces a human like decision by inferring on the propose fuzzy student models. For the purpose of this research, the training data which is an instance of students knowledge test performances were obtained from an adaptive-courseware E-learning system that administered knowledge tests to thirty, first year undergraduate students, from two southeastern European Universities, University of split Croatia and University of Mostar Bosnia-Herzegovina in the domain of “computer as a system” comprising of seventy three domain concepts. The results of this knowledge test, the students‟ scores in each of the seventy three domain concepts is to be used as crisp inputs to the proposed fuzzy membership functions to enables the realization of the first fuzzy logic control process, the fuzzification process. The propose membership functions are designed using multi fuzzy terms "poor", "weak", "average", "good", "very good" and "excellent" to allow for adequate fuzzy sets that can capture all intervals in the distribution. However, we need to compare the performance of the propose model with that of the two previous or existing models. With the first model that has 36% accuracy, this comparison is direct as it was implemented using same training data with the propose model. But with second model that has 90% accuracy, this comparison cannot be made directly as it was implemented using different training data with the propose model. This study therefore re-implement the second model using the training data from the domain of “computer as a system” in order to justify the performance of the propose model. The result has shown that the propose method has successfully improved the accuracies of the two previous models

Ontology based approach to Fuzzy student model design

The integration of Information and Communication Technology (ICT) in the teaching and learning process in our today’s educational environments gave birth to what is known as elearning technology. Correct implementation of these e-learning technologies can brings about a number of potential benefits to the educational systems that can ultimately leads to achieving the desired objectives through the use of these modern and qualitative innovations. When these technologies and innovations are effectively used in the instructional process not only they will reduced the cost effectiveness of the pedagogical and other relative constrains, but can also create a conducive atmosphere for exploring effective designed components from different perspectives. An important component of these elearning systems in use today is called the intelligent tutoring system (ITS). ITS is a software system designed using artificial intelligent techniques (comprising of Fuzzy Logic, Neural-Networks, Bayesian networks, Ontology, Genetic Algorithms and Software Agents) to provide a personalized tutoring adaptable to each student based on his/her profile or characteristics. In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student’s learning behaviour with the aim of improving the learning path and increase the system’s adaptability. The use of fuzzy logic in this context is to enable the computational analysis of the student’s characteristics and learning behaviours in order to handle the uncertainty issues related to the student model design. Ontology approach on the other hand is one of the recent techniques used to design the representation of student’s cognitive state. Moreover, Ontology is considered as a key component for managing knowledge in a particular domain, and we intend to use its methodology as a tool to examine and manage the student’s knowledge state, competency and skills

A model of tutor agent in collaborative e-learning environment

A collaborative e-learning environment is made up of participants that are remotely placed, who for their diverse social or geographical reasons could not be able to attain their educational goals in the same physical environment. In such collaborating environments, students need to interact and communicate with each other, share their common experiences and solve common problems. This may sometimes results to many collaboration conflicts that may require the intervention by a teacher. Managing such web integrated environments by a human teacher will no doubt be tedious, less efficient and at the same time consuming. The aim of this paper is to introduce a multi-agent approach to model the tutor component by substituting the human teacher with an intelligent tutor agent in order to improve the supervision and mediation against collaboration conflicts tasks of the teacher in such environment