Emotional scaffolding with respect to time factors in Networking Collaborative Learning Environments

With regard to learning, emotional considerations have been included in the research agenda for a long time and literature offers a variety of studies evaluating the role of emotions in different settings (class, tests and exams, studying at home, etc.). This knowledge and experience has tentatively begun to endow intelligent network systems with emotion assessment and affective feedback capabilities, although the process is still in its infancy. This paper reviews emotional aspects in learning and affect recognition as well as feedback strategies. In the described strategies, the need for considering the time factor is also stressed.

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

Cox's model for prison partly interval censored data

The term survival analysis has been used in examines and models the time until the events occur. The most common tool for studying the dependency of survival time on predictor variables is Cox model proportional hazards regression model. In this paper we present a simple modification of Cox’s proportional hazards model using the partial likelihood principle technique based on Newton Rapson method. Simulation is conducted based on prison partly interval censored data set with particular sample sizes to evaluate the performance of the proposed model, and it shows that the model is feasible and works well

WEIBULL DISTRIBUTION BASED ON EDUCATION PARTLY INTERVAL CENSORED DATA

The work in this project is concerned with the applying of techniques for the assessment of survival analysis in data that include censored observations. Survival analysis has a lot of achievement in the medical, engineering, economic, education and other fields and it also known as failure time analysis. Partly Interval Censoring (PIC) is one of the techniques of the censoring that used in the survival analysis and it can help to treat many types of data especially the incomplete data. One of the most commonly lifetime distribution used in the reliability applications is Weibull distribution. In this project we use Weibull model based on modified education partly interval censored data as well as medical data and simulation data. Based on the medical data, we found that our model is comparable with Turnbull method. From the education data and simulation study for this particular case, we can conclude that our proposed distribution describes well the nature of the model as compared to the Turnbull method in terms of the value of scale and shape parameter estimates. Plots of survival distribution function against failure time are used to examine the predicted survival patterns for the two types of failures

AI as a Methodology for Supporting Educational Praxis and Teacher Metacognition

Evidence-based practice (EBP) is of critical importance in education where emphasis is placed on the need to equip educators with an ability to independently generate and reflect on evidence of their practices in situ – a process also known as praxis. This paper examines existing research related to teachers’ metacognitive skills and, using two exemplar projects, it discusses the utility and relevance of AI methods of knowledge representation and knowledge elicitation as methodologies for supporting EBP. Research related to technology-enhanced communities of practice as a means for teachers to share and compare their knowledge with others is also examined. Suggestions for the key considerations in supporting teachers’ metacognition in praxis are made based on the review of literature and discussion of the specific projects, with the aim to highlight potential future research directions for AIEd. A proposal is made that a crucial part of AIEd’s future resides in its curating the role of AI as a methodology for supporting teacher training and continuous professional development, especially as relates to their developing metacognitive skills in relation to their practices

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

Exploring and Evaluating the Scalability and Efficiency of Apache Spark using Educational Datasets

Research into the combination of data mining and machine learning technology with web-based education systems (known as education data mining, or EDM) is becoming imperative in order to enhance the quality of education by moving beyond traditional methods. With the worldwide growth of the Information Communication Technology (ICT), data are becoming available at a significantly large volume, with high velocity and extensive variety. In this thesis, four popular data mining methods are applied to Apache Spark, using large volumes of datasets from Online Cognitive Learning Systems to explore the scalability and efficiency of Spark. Various volumes of datasets are tested on Spark MLlib with different running configurations and parameter tunings. The thesis convincingly presents useful strategies for allocating computing resources and tuning to take full advantage of the in-memory system of Apache Spark to conduct the tasks of data mining and machine learning. Moreover, it offers insights that education experts and data scientists can use to manage and improve the quality of education, as well as to analyze and discover hidden knowledge in the era of big data

Robotics and Design: An Interdisciplinary Crash Course

The authors designed and ran a crash course on emotional robotics involving students from both the Information Engineering School and the Design School of Politecnico di Milano , Milan, Italy. The course consisted of two intensive days of short introductory lessons and lab activity, done in interdisciplinary groups and supported by a well-equipped prototyping and modeling lab. People from very different backgrounds had to work efficiently together, going from problem setting through the demonstration of the physical implementation of an object able to show four different emotional states. Both teacher evaluation and questionnaire-based feedback from the students show that it was successful and useful to set up this type of intensive experience in which students share their abilities to achieve a common goal. Key aspects for the success of the course were the short time the students had to reach a well-defined, yet general, goal, the students' ability to find efficient ways of cooperating and sharing their competences, students' motivation to arrive at a working prototype, and the strong support from teachers and lab personnel