Soft Clustering of Physics Misconceptions Using a Mixed Membership Model

نویسندگان

  • Guoguo Zheng
  • Seohyun Kim
  • Yanyan Tan
  • April Galyardt
چکیده

Students often possess multiple, conflicting misconceptions which may be activated and expressed in different contexts. In this paper, we use a mixed membership model to explore the patterns of misconceptions in introductory physics. Mixed membership models have been widely used for modeling observations that have partial membership in several latent groups. The latent groups in the current study are misconception patterns. This model allows us to examine whether students are likely to hold a few or many misconceptions, as well as which misconceptions are likely to co-exist. Physics knowledge was measured with the Force concepts inventory (FCI). We found three dominant response patterns, with different misconceptions prominent within each pattern.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Exponential family mixed membership models for soft clustering of multivariate data

For several years, model-based clustering methods have successfully tackled many of the challenges presented by data-analysts. However, as the scope of data analysis has evolved, some problems may be beyond the standard mixture model framework.One suchproblem iswhenobservations in a dataset come fromoverlapping clusters, whereby different clusters will possess similar parameters for multiple va...

متن کامل

New distance and similarity measures for hesitant fuzzy soft sets

The hesitant fuzzy soft set (HFSS), as a combination of hesitant fuzzy and soft sets, is regarded as a useful tool for dealing with the uncertainty and ambiguity of real-world problems. In HFSSs, each element is defined in terms of several parameters with arbitrary membership degrees. In addition, distance and similarity measures are considered as the important tools in different areas such as ...

متن کامل

A new method for fuzzification of nested dummy variables by fuzzy clustering membership functions and its application in financial economy

In this study, the aim is to propose a new method for fuzzification of nested dummy variables. The fuzzification idea of dummy variables has been acquired from non-linear part of regime switching models in econometrics. In these models, the concept of transfer functions is like the notion of fuzzy membership functions, but no principle or linguistic sentence have been used for inputs. Consequen...

متن کامل

Bayesian Mixed Membership Models for Soft Clustering and Classification

The paper describes and applies a fully Bayesian approach to soft clustering and classification using mixed membership models. Our model structure has assumptions on four levels: population, subject, latent variable, and sampling scheme. Population level assumptions describe the general structure of the population that is common to all subjects. Subject level assumptions specify the distributio...

متن کامل

Providing a Method to Identify Malicious Users in Electronic Banking System Using Fuzzy Clustering Techniques

Money-Laundering causes a higher prevalence of crime and reduces the desire tending to invest in productive activities. Also, it leads to weaken the integrity of financial markets and decrease government control over economic policy. Banks are able to prevent theft, fraud, money laundering conducted by customers through identification of their clients’ behavioral characteristics. This leads to ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2016