The Advancing Out-of-School Learning in Mathematics and Engineering (AOLME)
project is an interdisciplinary effort -from faculty with areas of expertise in bilingual
education, mathematics education (Prof. Sylvia Celedón-Pattichis and Prof. Carlos A.
LópezLeiva), and electrical and computer engineering (Prof. Marios Pattichis and
Dr. Daniel Llamocca)- designed to support interactive and visual learning in
engineering and mathematics of middle school students, especially from
underrepresented groups. Through an integrated curriculum, the AOLME
project provides engaging experiences in engineering through the use of
digital image and video processing examples. Additionally, this curriculum
provides a set of mathematical practices different from but related to school mathematics.
The projet has two programs,
(i) A mathematics-engineering summer school (ME-S2),
which was implemented during the Summers of 2012 and 2013,
(ii) A mathematics-engineering club (MEC) which was implemented
during the Springs of 2013 and 2014.
These programs comprise a pilot study in the development and implementation
of level one and two of an integrated curriculum. The main goal of this after-school
initiative is to explore efficient ways to support the learning and active engagement
of middle school students in mathematics and engineering related activities.
Additionally, the program attempts to expand its curriculum in a series of levels
(applied through the ME-S2 and MEC programs) from middle school through high
school with the long-term goal of motivating and supporting a pipeline for these
students into STEM (Science, Technology, Engineering, and Mathematics) fields at
college levels.
Website
Book Chapters
Published Conference Proceedings
Refereed Journal Article
Refereed Papers/Presentations at International/National Professional Meetings
Theses
This thesis explores detection of hand movement using color and optical flow.
Exploratory analysis considered the problem component wise on components created
from thresholds applied to motion and color. The proposed approach uses patch
color classification, space-time patches of video, and histogram of optical flow. The
approach was validated on video patches extracted from 15 AOLME video clips. The
approach achieved an average accuracy of 84% and an average receiver operating
characteristic area under curve (ROC AUC) of 89%.
UNM Digital repository
Download Software
This thesis explores the use of color based object detection in conjunction with
contextualization of object interaction to isolate motion vectors specific to an activity
sought within uncropped video. Feature extraction in this thesis differs significantly
from other methods by using geometric relationships between objects to infer con-
text. The approach avoids the need for video cropping or substantial preprocessing
by significantly reducing the number of features analyzed in a single frame. The
method was tested using 43 uncropped video clips with 620 video frames for writing,
1050 for typing, and 1755 frames for talking. Using simple KNN classification, the
method gave accuracies of 72.6% for writing, 71% for typing and 84.6% for talk-
ing. Classification accuracy improved to 92.5% (writing), 82.5% (typing) and 99.7%
(talking) with the use of a trained Deep Neural Network.
UNM Digital repository
Download Software
The thesis explores phase-based solutions for (i) detecting faces,
(ii) back of the heads, (iii) joint detection of faces and back of the heads, and (iv)
whether the head is looking to the left or the right, using standard video cameras
without any control on the imaging geometry. The proposed phase-based approach
is based on the development of simple and robust methods that relie on the use of
Amplitude Modulation - Frequency Modulation (AM-FM) models.For the students facing the camera,
the method was able to correctly classify 97.1% of them looking to the left and 95.9%
of them looking to the right. For the students facing the back of the camera, the
method was able to correctly classify 87.6% of them looking to the left and 93.3%
of them looking to the right. The results indicate that AM-FM based methods hold
great promise for analyzing human activity videos.
UNM Digital repository
This thesis proposes an open-source, maintainable system for detecting human
activity in large video datasets using scalable hardware architectures. The system
is validated by detecting writing and typing activities that were collected as part of
the Advancing Out of School Learning in Mathematics and Engineering (AOLME)
project. The implementation of the system using Amazon Web Services (AWS)
is shown to be both horizontally and vertically scalable. The software associated
with the system was designed to be robust so as to facilitate reproducibility and
extensibility for future research.
UNM Digital repository
The Advancing Out-of-School Learning in Mathematics and Engineering
(AOLME) project was created specifically for providing integrated mathematics and
engineering experiences to middle-school students from under-represented groups.
The thesis presents a new approach to introducing game programming to middle
-school students that have undergone AOLME-training while still maintaining a fun
and relaxed environment. The thesis provides a discussion of three different
educational, visual-programming environments that are also designed for younger
programmers and provides motivation for the proposed approach based on Python.
The thesis details interactive activities that are intended for supporting the students
to develop their own games in Python.
UNM Digital repository
Projects
Undergraduate Projects
Matlab code