Tracer Study on AIMST University Students Using Data Mining

Tracer study is an approach which widely being used in most of the organization especially in the education institutions to track and to keep record of their students once they have graduated from the institution. Through tracer study, an institution able to evaluate the quality of education given to their graduates by knowing the graduates placements and positions in the society which later can be used as a benchmark in producing more qualified and competitive graduates. This tracer study basically focuses at one of the leading private university in the northern region of Peninsular Malaysia which known as AIMST University. This tracer study uses SPSS software as one the primary method to produce a relevant model of all the students’ enrolment as well as graduating students’ based on the data supplied by the Students and Records Division of AIMST University. However, the data supplied by the Student Admission and Records of AIMST University do contains missing values hence the data sets have to undergo cleaning process. As such CRISP methodology being applied to the datasets to ensure the data transformed into quality and usable data sets, with that the data will undergo pre processing approach. These data sets will be used in Data Mining approach in the modeling techniques to analyze the data and to identify the patterns

Your model is predictive— but is it useful? Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation

Classification evaluation metrics are often used to evaluate adaptive tutoring systems— programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may lead to suboptimal decisions. We propose the Learner Effort-Outcomes Paradigm (Leopard), a new framework to evaluate adaptive tutoring. We introduce Teal and White, novel automatic metrics that apply Leopard and quantify the amount of effort required to achieve a learning outcome. Our experiments suggest that our metrics are a better alternative for evaluating adaptive tutoring

Using data mining to dynamically build up just in time learner models

Using rich data collected from e-learning systems, it may be possible to build up just in time dynamic learner models to analyze learners' behaviours and to evaluate learners' performance in online education 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 find patterns from which metrics can be derived from usage data. In this thesis, we propose a six layer model (raw data layer, fact data layer, data mining layer, measurement layer, metric 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, filter fact data from raw data, and then use clustering mining methods to create measurements and metrics. In a pilot study, we used usage data collected from the iHelp system to create measurements and metrics to observe learners' behaviours in a real online system. The measurements and metrics relate to a learner's sociability, activity levels, learning styles, and knowledge levels. To validate the approach we designed two experiments to compare the metrics and measurements extracted from the iHelp system: expert evaluations and learner self evaluations. Even though the experiments did not produce statistically significant results, this approach shows promise to describe learners' behaviours through dynamically generated measurements and metric. Continued research on these kinds of methodologies is promising