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This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.

Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions.

The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance.

This study is limited to course students from one institute and does not consider external factors.

Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions.

1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

This paper utilizes data mining to study the effect of Problem Based Learning (PBL), an innovative pedagogical approach that has been implemented in undergraduate education at a private university in India for teaching Statistics and Operations Research (OR) to techno-management students.

The study follows the assumptions of an in-situ experiment. It employs BBA (IT) and BCA student(s) as a subject and their end of semester GPA as a performance indicator. The pedagogical approach to this study is integrating PBL with classroom teaching. The paper uses a combination of statistics and data mining to analyze the impact of PBL and establish research conclusions.

The study concludes that the introduction of PBL positively results in an improved GPA for students with a math background. PBL is more effective for BBA (IT) male students. Female students seem to be performing equally well irrespective of the inclusion of PBL. Pattern analysis of shape parameters evidences the impact of PBL, and the results are established through the decision tree and test of proportions.

The study is limited to students from a single institute.

This Pattern analysis, as applied in this paper, can be scaled to evaluate the impact of any innovative pedagogical approach agnostic of the field of study. Facilitators can use the process defined in the paper to implement PBL for teaching Statistics and Operations research. Shape parameters of the batch in the previous semester can be utilized by facilitators to plan remedial action for the next semester by classifying students as desirable/non-desirable. Techno-management institutes can alleviate the dread and fear of mathematical subjects by integrating PBL with classroom teaching.

The study utilizes an innovative analytical approach of combining shape parameters with classification. It further provides uniqueness in arriving at a classification of batch performance as desirable/non-desirable and utilizes data mining to emphasize a delineating impact of PBL across both critical parameters of the batch and the student. The study also defines a framework for the implementation of PBL for a techno-management program in Statistics and Operations Research.