Concrete design rules can facilitate the designer to depict capable design and reliable products. However, it is difficult to deduce systematic design rules from previous experience and to modify the rules in a rule-based system. In the last decade, Case-Based Reasoning (CBR) has become an important methodology to solve the problem. The objective of this paper is to determine the appropriate case representation used in assembly part design for developing a CBR system. The designer can obtain real-time Early experimental results indicate that the case representation can appropriately represent the expertise and experience of assembly part design based on CBR methodology.

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Classification is a general framework that can be applied tovarious tasks such as object recognition, prediction, diagnosis or learning. There exist at least two different approaches for classification, namely rule-based and similarity-based classification. The two approaches have different strengths and weaknesses. The former requires a domain theory in order to make inferences from the test instance to its class. The latter does not have this requirement and approximates the class of a test instance via its similarity to a set of known instances.In this thesis the above two approaches are integrated in the realm of Case-Based Reasoning (CBR). CBR treats new cases according to their similarity to stored cases. Similarity is calculated by a similarity measure, which is the crucial factor for classification accuracy. In this work, rule-based domain knowledge is incorporated into the similarity measures of CBR in order to increase classification accuracy. Several novel integration methods are introduced, implemented and evaluated. Since knowledge about real world domains is typically imperfect, the approach does not assume that the domain theories are accurate or complete. Rather, a systematic analysis of different knowledge types is presented that shows the effect of imperfect knowledge on classification accuracy. The analysis is conducted partly empirically in artificial and in real world domains, and partly formally.

The COVID-19 has significantly impacted the world as we know it, especially in higher education institutions. In response to COVID-19, most colleges and universities were forced to close their doors to students and switch to online learning over Zoom. Although most students are adjusted well to this new mode of education, other students, such as individuals pursuing degrees in the sciences, have difficulty adapting to the online mode of instruction. Case-based learning (CBL) is considered one of the best strategies in teaching a course like Biochemistry in a large classroom setting. Implementing CBL in biochemistry education may increase student engagement in the curriculum. Students will connect the material they learned in class to their daily lives, promoting critical thinking. The purpose of this review was to determine the effects of CBL in biochemistry education. This integrative review will focus on the various CBL study that implored in a biochemistry class setting. The review aimed to determine the implication of CBL in teaching biochemistry and assess students' views regarding the CBL.

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Three hypertext implementation strategies were evaluated against one another and against a control group to determine which best supported the language learner. Each version was also applied to four languages with diverse grammatical structures. These included Mandarin Chinese, Japanese, Portuguese, and Spanish. Language students were tested to determine how useful each strategy was in facilitating rapid and accurate explanation of grammatical structures embedded in native speech examples. Speed and accuracy were also measured as respondents applied a targeted grammar structure to construction of their own unique sentences. With respect to the four different languages, results were also analyzed to judge whether the hypertext strategies were viable for each language. The strategy iteration that directed learners to a more detailed and specific explanation was deemed to be more successful than those with generalized explanations in assisting language learners. Moreover, the strategies seemed to provide the same relative benefit across the tested languages, suggesting they are portable and applicable even to non-researched languages. Variance in outcomes among languages within this study focus was also strongly correlated to the degree of difference in grammatical structure between a tested language and English – the learners' typical native language.

This study investigated the various methods involved in creating an intelligent tutor for the University of Central Florida Infinity Web Applets (UCF Infinity Web Applets). After conducting research into various methods, two major methods emerged and they are: solving the problem for the student and helping the student when they become stymied and unable to solve the problem. A storyboard was created to show the interactions of the student and system along with a list of features that were desired to be included in the tutoring system. From the storyboard and list of features, an architecture was created to handle all of the interactions and features. After the initial architecture was designed, the development of the actual system was started. The architecture underwent a multitude of changes to conclude with a working system, EINO. The final architecture of EINO incorporated a case based reasoning system to perform pattern recognition on the student's input into the UCF Infinity Web Applets. The interface that the student interacts with was created using flash. EINO was implemented in three of the labs from the UCF Infinity Web Applets. A series of tests were performed on the EINO tutoring system to prove that the system could actually perform each and every one of the features listed initially. The final test was a simulation of how the EINO would perform under a set of given cases. Test subjects with the same educational level as the target group were chosen to spend an unlimited time using each of the three labs. Each of the test subjects filled out a survey on every lab to determine if the EINO system produced a helpful output.

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