Using IMMEX as an Alternative to Laboratory Exams Charlie Cox, Presenter Melanie M. Cooper Ron Stevens Rebecca Pease Valerie Smith.

Slides:



Advertisements
Similar presentations
Pseudo-Relevance Feedback For Multimedia Retrieval By Rong Yan, Alexander G. and Rong Jin Mwangi S. Kariuki
Advertisements

Bridgette Parsons Megan Tarter Eva Millan, Tomasz Loboda, Jose Luis Perez-de-la-Cruz Bayesian Networks for Student Model Engineering.
BIOINFORMATICS GENE DISCOVERY BIOINFORMATICS AND GENE DISCOVERY Iosif Vaisman 1998 UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL Bioinformatics Tutorials.
A SOFTWARE TOOL DEVELOPED FOR THE CLASSIFICATION OF REMOTE SENSING SPECTRAL REFLECTANCE DATA Abdullah Faruque School of Computing & Software Engineering.
Consultation on Senior Cycle Science Anna Walshe Brendan Duane
Help Desk Troubleshooting Computer Problems. 2 Certificate III Software Applications Troubleshooting Computer Problems Solving computer problems is one.
EXPERT SYSTEMS apply rules to solve a problem. –The system uses IF statements and user answers to questions in order to reason just like a human does.
In carbon-13 NMR, what do the number of peaks represent?
Effective Skill Assessment Using Expectation Maximization in a Multi Network Temporal Bayesian Network By Zach Pardos, Advisors: Neil Heffernan, Carolina.
Introduction to the State-Level Mitigation 20/20 TM Software for Management of State-Level Hazard Mitigation Planning and Programming A software program.
Dissemination and Critical Evaluation of Published Research Peg Bottjen, MPA, MT(ASCP)SC.
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
Technion –Israel Institute of Technology Software Systems Laboratory A Comparison of Peer-to-Peer systems by Gomon Dmitri and Kritsmer Ilya under Roi Melamed.
Warning & Indicator Systems WISE lab Lab Director: Professor Richard Mammone Faculty : Christine Podilchuck, Joseph Wilder Students: Anand Doshi, Aparna.
Introduction to BioInformatics GCB/CIS535
Modeling and Validation Victor R. Basili University of Maryland 27 September 1999.
Lecture #1COMP 527 Pattern Recognition1 Pattern Recognition Why? To provide machines with perception & cognition capabilities so that they could interact.
CS157A Spring 05 Data Mining Professor Sin-Min Lee.
A Value-Based Approach for Quantifying Scientific Problem Solving Effectiveness Within and Across Educational Systems Ron Stevens, Ph.D. IMMEX Project.
Supervised Learning: Perceptrons and Backpropagation.
ICT TEACHERS` COMPETENCIES FOR THE KNOWLEDGE SOCIETY
Automatic assignment of NMR spectral data from protein sequences using NeuroBayes Slavomira Stefkova, Michal Kreps and Rudolf A Roemer Department of Physics,
C R E S S T / U C L A Evaluating the Impact of the Interactive Multimedia Exercises (IMMEX) Program: Measuring the Impact of Problem-Solving Assessment.
Dynamically altering the learning trajectories of novices with pedagogical agents Carole R. Beal, USC Ronald H. Stevens, UCLA Cognition & Student Learning.
Soft Computing Lecture 17 Introduction to probabilistic reasoning. Bayesian nets. Markov models.
General Laboratory Tools Techniques and Methods Browse through these files to get familiar with equipment, techniques and general approaches to use in.
Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines Blaise Gassend, Charles W. O'Donnell, William Thies,
Introduction to Data Mining Group Members: Karim C. El-Khazen Pascal Suria Lin Gui Philsou Lee Xiaoting Niu.
Data Mining Chapter 1 Introduction -- Basic Data Mining Tasks -- Related Concepts -- Data Mining Techniques.
Conceptual Foundations © 2008 Pearson Education Australia Lecture slides for this course are based on teaching materials provided/referred by: (1) Statistics.
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
Chengjie Sun,Lei Lin, Yuan Chen, Bingquan Liu Harbin Institute of Technology School of Computer Science and Technology 1 19/11/ :09 PM.
Use of Machine Learning in Chemoinformatics Irene Kouskoumvekaki Associate Professor December 12th, 2012 Biological Sequence Analysis course.
1 C1403NMR Homework This assignment will challenge you to interpret NMR spectra by correlating spectra data with molecular structure. The IR and proton.
Integrated Problems: Problem Solving. Problem Solving Given more information than you will use Key: focus on most helpful data Key: develop a strategy.
AN INTELLIGENT AGENT is a software entity that senses its environment and then carries out some operations on behalf of a user, with a certain degree of.
Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS.
H0K08A EXERCISE SESSIONS. Exercises - overview Steven Van Vooren Steven.VanVooren esat.kuleuven.be Toledo Site
C1403 NMR Homework The IR and proton (1H NMR) and carbon (13C NMR) spectra of the molecules of IR Tutor are given along with correlation tables. This.
Combined techniques problems L.O.:  Analyse absorptions in an infrared spectrum to identify the presence of functional groups in an organic compound.
Practice problems on the NMR of amino acids Test your ability to correlate NMR spectra with structure by trying the following problems. Use the correlation.
Protein Structure Prediction ● Why ? ● Type of protein structure predictions – Sec Str. Pred – Homology Modelling – Fold Recognition – Ab Initio ● Secondary.
 Based on observed functioning of human brain.  (Artificial Neural Networks (ANN)  Our view of neural networks is very simplistic.  We view a neural.
Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) Under the guidance of Ms. Suchilipi Nepak Presented By Prasanna.
So, what’s the “point” to all of this?….
Assessment Analysis Using MarkIt CECIL Users Meeting 6 May 2004.
IMMEX: Providing Insight into Problem Solving Using Technology Charlie Cox Preliminary Oral Defense.
Digital Video Library Network Supervisor: Prof. Michael Lyu Student: Ma Chak Kei, Jacky.
THE SCIENTIFIC METHOD. THE SCIENTIFIC METHOD: is a process used to find answers to questions about the world around us is an organized series of steps.
Final Year Project. Project Title Kalman Tracking For Image Processing Applications.
Use of Machine Learning in Chemoinformatics
WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.
WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.
Solutions Quiz, Neutralization Reactions, Titrations.
Chemistry In this science we study matter and the changes it undergoes.
Writing your Master Thesis Management of People, Management of Innovation Processes, and Strategy and Organization Bo H. Eriksen, 12 October 2016.
Implementing Boosting and Convolutional Neural Networks For Particle Identification (PID) Khalid Teli .
Discovery Search vs. Library Catalogue
C1403 NMR Homework The IR and proton (1H NMR) and carbon (13C NMR) spectra of the molecules of IR Tutor are given along with correlation tables. This.
Find 4 A + 2 B if {image} and {image} Select the correct answer.
The scientific Method.
Chapters 11 and 12: IR & NMR Spectroscopy, Identification of Unknowns
3.1.1 Introduction to Machine Learning
Command Terms
CH 14-3: Unknown Analysis of Benzene
Problems, Purpose and Questions
Scientific method.
Sr. Portfolio and Teacher Work Sample
Phoneme Recognition Using Neural Networks by Albert VanderMeulen
Use the ten frames to help solve the problems
Presentation transcript:

Using IMMEX as an Alternative to Laboratory Exams Charlie Cox, Presenter Melanie M. Cooper Ron Stevens Rebecca Pease Valerie Smith

What is IMMEX? Internet - Based Software Designed at UCLA Medical School Case - Based Problems

IMMEX Problems Prolog Problem Space HTML Tracking Ability Dialog Boxes

Prolog Screen Shot

Problem Space

Search Path Maps

Assessment Techniques Artificial Neural Networks Hidden Markov Modeling Item Response Theory

Artifical Neural Networks Pattern Recognition Groups similar strategies Represented using Nodes

Node Description Problem Space Item PROBABILITYPROBABILITY Background Flame, Solubility, Conductivity Litmus HCl, NaOH Precipitins

6 x 6 Node Description

Hidden Markov Models Predictive Modeling More simplistic than nodes Described using five states

Hidden Markov Models (HMMs)

Item Response Theory Separates latent abilities from item characteristics. Model:

Laboratory Problems Separation Hazmat TLC Finding Carbons Neighbors Spectra Analysis

Separations

Solved Separation

Finding Carbons Neighbors Chemical Tests Physical Tests Library

Chromatography Challenge Unknown identification based upon TLC analyses.

Spectra Analysis 1 H, 13 C, IR, and MS data is available for students to elucidate an unknown’s structure.

1 H NMR Spectra

Other Features 1.Example Spectra a. Benzaldehyde b. 2-butanone c. Benzene d. Butylamine e. Propionic Acid 2.Correlation Tables 3. Library

Implementation Second semester organic laboratory. Used as part of the laboratory practical (50%) Students are told to answer 4 cases correctly with a guessing penalty.