Otros Documentos

OVER THE LAST DECADE, THE USE OF SOCIAL MEDIA AS A MASSIVE COMMUNICATION MEDIUM HAS GIVEN PEOPLE A TOOL TO EXPRESS THEIR OPINIONS. IN IT, PEOPLE WRITE THEIR THOUGHTS AND FEELINGS ABOUT PLENTY OF TOPICS GENERATING LARGE AMOUNT OF DATA THAT CAN BE ANALYZED BY COMPANIES AND RESEARCHERS. BEING TASKS OF THE NATURAL LANGUAGE PROCESSING, EMOTION ANALYSIS FOCUSES ON EXTRACTING THE UNDERLYING EMOTIONS IN TEXT, MEANWHILE, SENTIMENT ANALYSIS FOCUSES ON EXTRACTING THE POLARITY OF IT. TO ACCOMPLISH THIS TWO TASKS, TRADITIONAL MACHINE LEARNING AND DEEP LEARNING TECHNIQUES ARE USED. HOWEVER, TO REACH GOOD GENERALIZATION PERFORMANCE, THESE TECHNIQUES REQUIRE LARGE DATASETS OF LABELED DATA FOR TRAINING. FOR RESEARCHERS THIS IS AN ISSUE BECAUSE IN LANGUAGES LIKE SPANISH THE LABELED DATASETS ARE SPARSE. TO SOLVE THIS, DATA AUGMENTATION TECHNIQUES ARE USED TO GENERATE WIDER DATASETS OF LABELED DATA FROM A SMALL, LABELED DATASET. THIS WORK PRESENTS AN OPENMP VERSION FOR SHARED MEMORY SYSTEMS OF A DATA AUGMENTATION TECHNIQUE CALLED POS WISE SYNONYM SUBSTITUTION THAT REPLACES SOME OF THE WORDS OF A SENTENCE WITH THEIR SYNONYMS EXTRACTED FROM WORDNET TO CREATE NEW SENTENCES. WITH THE PARALLEL APPROACH WE REDUCED THE EXECUTION TIME REASONABLY COMPARED TO THE ORIGINAL VERSION REACHING A SPEEDUP OF UP TO 17.5X

IN THIS ARTICLE WE PRESENT PRELIMINARY RESULTS OF A PROJECT IMPLEMENTED AT THE UNIVERSIDAD DEL BÍO-BÍO (UBB), CONCEPCIÓN CAMPUS, THAT SEEKS TO CONTRIBUTE TO INCREASING THE PERCEPTION OF SECURITY AMONG USERS, BY USING INFORMATION AND COMMUNICATION TECHNOLOGIES (ICTS). CURRENTLY, COMMUNITY MEMBERS OF THE CONCEPCION CAMPUS AT UBB SHOW DIFFERENT PERCEPTIONS OF INSECURITY. INITIALLY, WE PERFORM A DIAGNOSIS TO KNOW WHAT ARE THE INSECURITY PROBLEMS THAT AFFECT THE COMMUNITY, AND THE EFFECTS OF INSECURITY ON THE WELL-BEING OF THE COMMUNITY OF THE CONCEPCIÓN CAMPUS. IN THIS WAY, WE ESTABLISH THE MAIN PROBLEMS AROUND SECURITY AND EVALUATE DIFFERENT WAYS IN WHICH THE USE OF ICTS CONTRIBUTES TO IMPROVE THE INSECURITY PERCEPTION. THIS ARTICLE REPORTS A MOBILE APPLICATION PROTOTYPE, THAT ALLOWS ALERTING OF POSSIBLE UNSAFE EVENTS, TO BE USED WITHIN THE CONCEPCIÓN CAMPUS. THIS APPLICATION ALSO PERMITS TO GENERATE REPORTS OF SECURITY PROBLEMS THAT ARE PERCEIVED AT THE CAMPUS, WHICH ALLOW BOTH APPLICATION USERS AND UNIVERSITY MANAGERS TO ACQUIRE INFORMATION ON THE UNIVERSITY ENVIRONMENT IN TERMS OF SECURITY.

IN RECENT TIMES, THE FIELD OF COMPUTER PROGRAMMING HAS EXPERIENCED A SIGNIFICANT REVOLUTION, THANKS TO ADVANCEMENTS IN MACHINE LEARNING. APPLICATIONS HAVE EMERGED WITH THE CAPABILITY TO GENERATE SOURCE CODE FROM NATURAL LANGUAGE DESCRIPTIONS. THESE TOOLS PRIMARILY UTILIZE LANGUAGE MODELS BASED ON DEEP LEARNING, WHICH HAVE BEEN TRAINED ON A COLLECTION OF PROGRAMS AND PROJECTS HOSTED IN PUBLIC REPOSITORIES. ONE OF THESE TOOLS IS GITHUB COPILOT, AN ARTIFICIAL INTELLIGENCE CAPABLE OF GENERATING SOURCE CODE THAT CAN BE INTEGRATED AS AN EXTENSION INTO DEVELOPMENT ENVIRONMENTS. THE OBJECTIVE OF THIS STUDY IS TO EXPERIMENTALLY EXPLORE, ANALYZE, AND EVALUATE THE SUGGESTIONS MADE BY THE GITHUB COPILOT TOOL IN PROGRAMMING TOPICS RELATED TO THE COMPUTER SCIENCE DEGREE AT THE UNIVERSITY OF BIO-BIO. WE PROPOSE FIVE STEPS: (1) COLLECTING NATURAL LANGUAGE STATEMENTS FOR BOTH GENERAL AND SPECIFIC PROGRAMMING PROBLEMS; (2) UTILIZING GITHUB COPILOT TO GENERATE PROGRAMS; (3) EVALUATING ITS PERFORMANCE; (4) CONDUCTING AN ANALYSIS; AND (5) MEASURING CODE QUALITY. THIS APPROACH ALLOWS US TO GAIN AN INITIAL UNDERSTANDING OF ITS EFFECTIVENESS, EMPHASIZING ITS APPLICATION FOR WELL-ESTABLISHED PROBLEMS AND MONITORING ITS USE FOR PROBLEMS WITH DISTINCT OBJECTIVES.

THE OBJECTIVE OF THIS PAPER IS TO PRESENT PRELIMINARY WORK ON THE DEVELOPMENT OF AN EDUCHATBOT TOOL AND THE MEASUREMENT OF THE EFFECTS OF ITS USE AIMED AT PROVIDING EFFECTIVE FEEDBACK TO PROGRAMMING COURSE STUDENTS. THIS BOT, HEREINAFTER REFERRED TO AS TUTORBOT+, WAS CONSTRUCTED BASED ON CHATGPT3.5 AND IS TASKED WITH ASSISTING AND PROVIDING TIMELY POSITIVE FEEDBACK TO STUDENTS IN COMPUTER SCIENCE PROGRAMMING COURSES AT UCSC. METHODS/ANALYSIS: THE PROPOSED METHOD CONSISTS OF FOUR STAGES: (1) IMMERSION IN THE FEEDBACK AND LARGE LANGUAGE MODELS (LLMS) TOPIC; (2) DEVELOPMENT OF TUTORBOT+ PROTOTYPES IN BOTH NON-CONVERSATIONAL AND CONVERSATIONAL VERSIONS; (3) EXPERIMENT DESIGN; AND (4) INTERVENTION AND EVALUATION. THE FIRST STAGE INVOLVES A LITERATURE REVIEW ON FEEDBACK AND LEARNING, THE USE OF INTELLIGENT TUTORS IN THE EDUCATIONAL CONTEXT, AS WELL AS THE TOPICS OF LLMS AND CHATGPT. THE SECOND AND THIRD STAGES DETAIL THE DEVELOPMENT OF TUTORBOT+ IN ITS TWO VERSIONS, AND THE FINAL STAGE LAYS THE FOUNDATION FOR A QUASI-EXPERIMENTAL STUDY INVOLVING STUDENTS IN THE CURRICULUM ACTIVITIES OF PROGRAMMING WORKSHOP AND DATABASE WORKSHOP, FOCUSING ON LEARNING OUTCOMES RELATED TO THE DEVELOPMENT OF COMPUTATIONAL THINKING SKILLS, AND FACILITATING THE USE AND MEASUREMENT OF THE TOOL'S EFFECTS. FINDINGS: THE PRELIMINARY RESULTS OF THIS WORK ARE PROMISING, AS TWO FUNCTIONAL PROTOTYPES OF TUTORBOT+ HAVE BEEN DEVELOPED FOR BOTH THE NON-CONVERSATIONAL AND CONVERSATIONAL VERSIONS. ADDITIONALLY, THERE IS ONGOING EXPLORATION INTO THE POSSIBILITY OF CREATING A DOMAIN-SPECIFIC MODEL BASED ON PRETRAINED MODELS FOR PROGRAMMING, INTEGRATING TUTORBOT+ WITH OTHER PLATFORMS, AND DESIGNING AN EXPERIMENT TO MEASURE STUDENT PERFORMANCE, MOTIVATION, AND THE TOOL'S EFFECTIVENESS.

ENSURING A CERTAIN LEVEL OF QUALITY OF LEARNING OBJECTS USED IN E-LEARNING IS CRUCIAL TO INCREASE THE CHANCES OF SUCCESS OF AUTOMATED SYSTEMS IN RECOMMENDING OR FINDING THESE RESOURCES. THIS PAPER AIMS TO PRESENT A PROPOSAL FOR IMPLEMENTATION OF A QUALITY MODEL FOR LEARNING OBJECTS BASED ON ISO 9126 INTERNATIONAL STANDARD FOR THE EVALUATION OF SOFTWARE QUALITY. FEATURES INDICATORS ASSOCIATED WITH THE CONFORMANCE SUB-CHARACTERISTIC ARE DEFINED. SOME INSTRUMENTS FOR FEATURE EVALUATION ARE ADVISED, WHICH ALLOW COLLECTING EXPERT OPINION ON EVALUATION ITEMS. OTHER QUALITY MODEL FEATURES ARE EVALUATED USING ONLY THE INFORMATION FROM ITS METADATA USING SEMANTIC WEB TECHNOLOGIES. FINALLY, WE PROPOSE AN ONTOLOGY-BASED APPLICATION THAT ALLOWS AUTOMATIC EVALUATION OF A QUALITY FEATURE. IEEE LOM METADATA STANDARD WAS USED IN EXPERIMENTATION, AND THE RESULTS SHOWN THAT MOST OF LEARNING OBJECTS ANALYZED DO NOT COMPLAIN THE STANDARD.