LAMP: A Framework for Large-Scale Addressing of Muddy Points

Abstract

Abstract-Muddy Points (MP) is a strategy to elicit and address individual students' doubts. While this can be effectively implemented in small classes, it is a challenge to do so in a large class. In this paper we propose LAMP, a framework for Large-scale Addressing of Muddy Points, as a mechanism for instructors to ensure that every individual student's doubts are addressed even in large classes. LAMP has three phases: Collection, Addressal, and Closure. In the collection phase, MPs are systematically collected through four different modes. In the addressal phase, MPs are categorized into six categories and addressed accordingly. In the closure phase, the discussions on MPs are summarized. We investigated the effectiveness of LAMP in an introductory computer science course having 450 students. We found that 68% of students confirmed they were able to pose their questions and 57% of students confirmed that there was closure to their questions.

Using Stratified Attribute Tracking (SAT) Diagrams for Learning Analytics

“…SAT diagram can assist the researcher to achieve many of those goals by providing an external representation. It can track user activities (case study (CS): [8] [9]), capture the interaction of students with resources / the interactions among students (CS: [9]), provide an overview (CS: [6]), highlight important aspects of data (CS: [10]), provide different perspectives (CS: [8]), draw the users attention to interesting correlations and provide decision support (CS: [9][10]).…”

“…In the first case study [8] the diagram develops a student behavior model, in the second case [9] it is used to assess the effectiveness of a Largescale synchronous remote classroom model and in the third case [10] it tracks students' perception data. The PP study [6] is the fourth published result.…”