The assessment of complex learning outcomes

The Engineering Professors' Council (EPC) produced an output standard in 2000 containing a setof 26 generic statements of what an engineering graduate should have an ability to tackle. In addition, Higher Education (HE) is concerned with the promotion of complex or advanced understanding of subject matter. This leads to complex learning outcomes, which need to be adequately assessed. Changing demands mean changing assessment practices. While good practice is being used in many cases, there is a need to ensure assessment stimulates complex learning. The article seeks to address these issues

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

The Impact of Some Socio-Economic Factors on Academic Performance: A Fuzzy Mining Decision Support System

Due to the reported impacts of some socio-economic factors on academic performance and nations’ education value, there is need for strong awareness to assist students in making the right decision. To this effect, this study proposes and designs student decision support system for determining the extent to which different levels of some socio-economic factors involvement can jointly affect academic performance. The factors are: Student’s interest, Relationship status, Entrepreneurial activities, Peer influence, Health and family background. The traditional decision support system architecture was extended in this study by introducing two components: Fuzzy engine and Mining Engine. Fuzzy engine was introduced to capture intra uncertainties in students' judgment about the data gathered and Mining engine to extract hidden and previously unknown interesting patterns from the dataset. The predictive model was established using fuzzy association rule mining technique. The dataset was gathered using one-on-one questionnaire interaction with students from 4 Universities in Nigeria. The system evaluates students' linguistic levels of involvement and predicts the possible class of honours for them with explicit interpretation of the fired patterns. This system will assist the students in decision making as to the extent they can be involved in some socio-economic activities relative to their family and health status in order to have their desired classes of honour

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

QUALITATIVE ANSWERING SURVEYS AND SOFT COMPUTING

In this work, we reflect on some questions about the measurement problem in economics and, especially, their relationship with the scientific method. Statistical sources frequently used by economists contain qualitative information obtained from verbal expressions of individuals by means of surveys, and we discuss the reasons why it would be more adequately analyzed with soft methods than with traditional ones. Some comments on the most commonly applied techniques in the analysis of these types of data with verbal answers are followed by our proposal to compute with words. In our view, an alternative use of the well known Income Evaluation Question seems especially suggestive for a computing with words approach, since it would facilitate an empirical estimation of the corresponding linguistic variable adjectives. A new treatment of the information contained in such surveys would avoid some questions incorporated in the so called Leyden approach that do not fit to the actual world.Computing with words, Leyden approach, qualitative answering surveys, fuzzy logic

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

EVALUATION OF PROSPECTIVE MATH TEACHERS’ ABILITY TO ENTER GRADUATE EDUCATION WITH FUZZY LOGIC ALONG WITH VARIOUS COMPONENTS

It seems that getting graduate education has become more important compared to the past. This is the case for teachers and prospective teachers. In order to be admitted for graduate education in Turkey, one must have ALES (Academic personnel and graduate education entrance exam), College GPA (graduation grade point average) and a foreign language score. The road to success is a difficult process for many students to complete when represented by classification of traditional graduation grade point average. Development approaches of student achievement need to have a framework consisting of more in number and a complex success criteria in order to be more effective. Apart from the aforementioned grade data that was mainly determined in the classification that classify whether prospective teachers were suitable for graduate education or not, some other components such as; their emotional data, their level of knowledge on graduate education and how much priority they give to teaching department while doing their university preferences have also become important. The study was shaped in this context and by assessing various components related to the students with fuzzy logic, a more effective prediction and classification was tried to be presented. In the study, considering attitudes of prospective teachers towards graduate education, their genders, their levels of knowledge on graduate education, their university entrance scores, their order of preference, and their levels in undergraduate education, their suitabilities of admission to graduate education was aimed to be determined by fuzzy logic. In our study in which relationships of all above mentioned components with each other were analyzed, survey (scanning) method, of quantitative research methods, was used and the relational scanning model was preferred. In the study, the information of 390 prospective teachers who were studying at the department of primary school mathematics teaching in three different state universities and attending at formation programs but graduated from faculty of arts and sciences mathematics teaching department was used. MATLAB software was used for fuzzy logic analysis. In the research, a fuzzy logic rule base was created and 98 (25.1%) of the analyzed data were decided to be suitable for graduate education program. 29 (7.4%) of these prospective teachers were from the first year, 48 (12.3%) of them were from the fourth year, and 21 (5.3%) of them were from the formation group. The group with the highest percentage of prospective teachers considered to be suitable for graduate education is fourth year undergraduate students with 12.3%. The group with the lowest percentage is formation students with 5.3%. As a result of the analyses conducted by fuzzy logic providing a valid prediction and classification, the reason of fourth year prospective teachers have the highest percentage in the research can be explained as their having higher attitude scores and being more knowledge about graduate education and having higher scores on the university entrance exams than the other participants. In order to ensure prospective teachers to have a higher attitude towards the graduate education, their gaining awareness of research and being informed about graduate education from the first years of college can provide significant benefits. Prospective teachers in different departments may be included in the study. Considering different components related to the prospective teachers and conducting researches using other methods of artificial intelligence such as fuzzy logic, students and educators can be provided an effective prediction and classification opportunities.  Article visualizations