E-learning by Time Dynamic Model Using Data Mining

The object of this paper is to build up Just in Time Dynamic Learner Models to analyze learners' behaviors and to evaluate learners' performance in online education systems by using rich data collected from e-learning systems. The goal is to create metrics to measure learners' characteristics from usage data. To achieve this goal we need to use data mining methods, especially clustering algorithms, to second patterns from which metrics can be derived from usage data. In this paper, we propose a six layer models(raw data layer, fact data layer, data mining layer, measurement layer, metrics layer and pedagogical application layer) to create a just in time learner model which draws inferences from usage data. In this approach, we collect raw data from online systems, latter fact data from raw data, and then use clustering mining methods to create measurements and metrics

PENERAPAN SELEKSI ATRIBUT WEIGHTS BY INFORMATION GAIN DAN SELECT BY WEIGHTS PADA ALGORITMA NAÏVE BAYES UNTUK PREDIKSI KOLEKTIBILITAS PEMBIAYAAN USAHA KECIL DAN MENENGAH

Kredit adalah pinjaman uang dengan pembayaran tidak tunai dimana angsuran pembayaranya wajib dilakukan oleh peminjam kepada bank atau badan lain sesuai perjanjian kedua belah pihak dan dalam jangka waktu yang sudah di setujui bersama khususnya dalam hal ini adalah melirik kepada sebuah badan usaha kecil dan menengah.Usaha kecil dan menengah (UKM) adalah suatu badan usaha yang dibuat oleh organisasi atau bisa juga oleh perorangan agar dapat menciptakan suatu bidang usaha dan membuat lapangan kerja sehingga dapat menghasilkan finalsial serta diharapkan dapat membantu membangun perekomian Indonesia pada umumnya,Usaha kecil dan menengah (UKM) ingin agar dapat cepat mendapatkan dana kredit dari bank atau proposal usahanya ingin cepat di setujui oleh bank,sehingga arus timbal balik dari aspek ini menyebabkan bank membuat suatu kebijakan kelayakan kredit sesuai kolektibilitas bagi usaha kecil dan menengah (UKM) agar nantinya bank dapat mengetahui apakah usaha kecil dan menengah masuk ke dalam salah satu kategori di kolektibilitas.Dari permasalahan tersebut digunakan sebuah metode klasifikasi sekaligus memprediksi kolekibilitas kredit usaha kecil dan menengah (UKM) dengan model algoritma Naive bayes berbaris weight information gain dan select by weight Setelah dilakukan pengujian dengan model Algoritma naive bayes berbasis weight information gain dan select by weights menghasilkan akurasi sebesar 84.64%

Objective and Subjective Responsibility of a Control-Room Worker

When working with AI and advanced automation, human responsibility for outcomes becomes equivocal. We applied a newly developed responsibility quantification model (ResQu) to the real world setting of a control room in a dairy factory to calculate workers' objective responsibility in a common fault scenario. We compared the results to the subjective assessments made by different functions in the diary. The capabilities of the automation greatly exceeded those of the human, and the optimal operator should have fully complied with the indications of the automation. Thus, in this case, the operator had no unique contribution, and the objective causal human responsibility was zero. However, outside observers, such as managers, tended to assign much higher responsibility to the operator, in a manner that resembled aspects of the "fundamental attribution error". This, in turn, may lead to unjustifiably holding operators responsible for adverse outcomes in situations in which they rightly trusted the automation, and acted accordingly. We demonstrate the use of the ResQu model for the analysis of human causal responsibility in intelligent systems. The model can help calibrate exogenous subjective responsibility attributions, aid system design, and guide policy and legal decisions

Predicting Off-task Behaviors in an Adaptive Vocabulary Learning System

ABSTRACT In many studies, engagement has been considered as an important aspect of effective learning. Retaining student engagement is thus an important goal in intelligent tutoring systems (ITS). My current studies with collaborators on Dynamic Support of Contextual Vocabulary Acquisition for Reading (DSCoVAR) include building prediction models for students' off-task behaviors. By extracting linguistically meaningful features and historical context information from interaction log data, these studies illustrate how some types of off-task behavior can be modeled from behavioral logs. The results of this research contribute to existing studies by providing examples of how to extract behavioral measures and predict off-task behaviors within a vocabulary learning system. Identifying off-task behaviors can improve students' learning by providing personalized learning materials: for example, off-task behavior classifiers can be used to achieve more accurate predictions of the student's vocabulary mastery level, which in turn can improve the system's adaptive performance. Toward our goal of developing highly effective personalized vocabulary learning systems, this research would benefit from expert feedback on issues that include: principled approaches for adaptive assessment and feedback in a vocabulary learning system; and alternative methods for defining and generating off-task labels

Theoretical, Measured and Subjective Responsibility in Aided Decision Making

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. A bias existed only when participants with poor classification capabilities relied less-than-optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not assure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems

Computer-Assisted assessment in open-ended activities through the analysis of traces: A Proof of concept in statistics with R Commander

Open-ended tasks are common in Science, Technology, Engineering and Mathematics (STEM) education. However, as far as we know, no tools have been developed to assist in the assessment of the solution process of open-ended questions. In this paper, we propose the use of analysis of traces as a tool to address this need. To illustrate this approach, we developed a modified version of R Commander that collects traces of students' actions and described a way to analyze them by using regular expressions. We used this tool in an undergraduate introductory statistics course. The traces were analyzed by comparing them to predefined problem-solving steps, arranged by the instructor. The analyses provide information about the time students spent on the activity, their work intensity and the choices they made when solving open-ended questions. This automated assessment tool provides grades highly correlated to those obtained by a traditional test and traditional grading scheme

A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDS

ABSTRAC