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Help students to learn better Organic Chemistry with ChemSense. Chan Kam Yuen (199122298). Learning Organic Chemistry.  Furniss and Parsonage (1977) 3 most difficult areas: functional groups, isomerism and nomenclature  Bojczuk (1982)

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Help students to learn better organic chemistry with chemsense

Help students to learn better Organic Chemistry with ChemSense

Chan Kam Yuen (199122298)

Learning organic chemistry
Learning Organic Chemistry ChemSense

 Furniss and Parsonage (1977)

  • 3 most difficult areas: functional groups, isomerism and nomenclature

     Bojczuk (1982)

  • Organic chemistry was ranked as the most difficult area for students to learn

     W.Brook (1988)

    – About 40% of sixth form and college students thought that organic chemistry was the most difficult section of the syllabus in Kenya

Al organic chemistry in h k

Before 1999, ChemSense

Can be done better by drill & practice

Difficult for students with poor memorization

After 1999,

Require students to analyze the questions

Apply their knowledge into unfamiliar situations

Require conceptual changes in this area

AL Organic Chemistry in H.K.

Cognitive Conflict ChemSense

Computer or Technology

Peer Collaboration

Computer-supported Collaborative Learning (CSCL)

Conceptual Change

ChemSense ChemSense

  • Computer-based chemistry learning environment

  • Representational tool

    • A significant improvement in students’ understanding of solubility-related concepts

    • But not a significant effect based on students’ test scores.

  • Collaborative tool

    • A positive correlation between the use of ChemSense and developing deeper chemical understanding

My research question

My research Question ChemSense

Would students learn better in Organic Chemistry if they learn collaboratively with the help of ChemSense?

Methodological framework
Methodological Framework ChemSense

  • ChemSense will be integrated into a 13-week AL organic chemistry course in my school in Kwai Chung.

  • Students work collaboratively in dyads on the ChemSense program carrying out discussion & exercises.

    • Provide cognitive conflicts that facilitate conceptual change.

The class sample
The Class (Sample) ChemSense

  • 17 F.6 students of chemistry stream (8 F, 9 M) are divided into 3 groups

    • 2 experimental groups & 1 non-experimental group

    • Divided with similar abilities per group

Instruction ChemSense

  • All the content will be taught by me in normal way

  • Experimental groups will continue their discussion in ChemSense after lessons in home.

  • Exercise will be done after every 2 topics had been taught (Totally 4 assignments)

    • Non-experimental group will do individually

    • Experimental groups do collaboratively in ChemSense

Roles of chemsense in my project
Roles of ChemSense in My Project ChemSense

  • Work collaboratively

  • Take active role in learning

  • Set-up a Knowledge Data Base

  • Continue their studies or discussion outside school

Assessments ChemSense

  • A pretest & pro-test (quantitatively)

    • Compare whether there is significant increases in test scores when compared with non-experimental group students (t-test)

  • Analysis their messages posted by

    • Content Analysis (Herni, 1992)

    • Interaction Analysis (Anderson, 1997)

Assessments ChemSense

  • Measure their conceptual changes by

    • Interview 3-4 students in experimental groups

    • The rest will do a questionnaire

Findings 1
Findings (1) ChemSense

  • Experimental groups perform poorer than the control group in Assignment 1

    • Students worked in a co-working mode (there was little discussion among the members)

  • The means scores attained by the experimental groups are higher than those of the control group in Assignments 2-4.

The ChemSenset-value (N=9) that reflects the performance difference between the experimental groups and the control group in the pre-test, assignments and post-test.

Findings (2)

  • No significant difference ChemSense in the performance of students from different groups is detected in all tests and assignments

    • the sample size is small (N=9)

    • students participating in the experiment may not be able to ‘internalize’ the knowledge that they constructed collaboratively within the research period

Evidence of knowledge building in collaborative learning content analysis
Evidence of knowledge building in collaborative learning ChemSense – Content Analysis

  • The number of messages posted for discussion by the experimental groups for each assignment tends to increase gradually

    •  22 in Assignment 1 to 60 in Assignment 4

  • The percentage of purely social (agreement) messages dropped significantly too

    • Girls: 85%  75%  21%  18%

    • Boys: 36%  16%  25%  16%

Level of ChemSense

Interactivity = 3

  • The level of interactivity of a message

    • the number of interrelated messages

  • Both groups started with a very low level of interactivity

    • For the girls group, the level of interactivity was 1 in the first assignment but increased to 5 in the last assignment.

    • For the boys group, the level of interactivity started from 2 initially to 7 at last.

  • The average message length (no. of words): ChemSense

    • the girls group’s figure increased from 14.3 in the first assignment to 36.3 in the last assignment.

    • As to the boys group, the average message length was roughly 35.0 in all assignments.

Analyzing discussion interaction analysis model
Analyzing Discussion – ChemSenseInteraction Analysis Model

  • Phase I: Sharing/comparing of information

  • Phase II: Discovery of dissonance and inconsistency

  • Phase III: Negotiation of meaning/co-construction of knowledge

  • Phase V: Agreement/application of newly constructed meaning

Number of postings of each phase in their discussion ChemSense

Their discussion is mainly sharing information

Boys’ group has more collaborative work than girls’.

Example of discussion
Example of Discussion ChemSense

Boy’s group discussed for better solution.

(Phase II discussion)

  • Suggest synthetic sequences for the following conversions (not more than 4 steps)


  • Yan ChemSense provide answer to this question first: I think in the first step, it need add H2 (Pd /BaSO4 / S) in order to reduce the acyl chloride to aldehyde. Then we need (1. LiAlH4/ ether 2. H2O) to reduce the aldehyde to alcohol.

  • Fu ChemSense provided a clear answer:

    1. CH3CH2CH2COCl H2/Pd/BaSO4/S CH3CH2CH2CHO (reduction of acyl chloride)

    2. CH3CH2CH2CHO LiAlH4/ether H2O CH3CH2CH2CH2OH (reduction of aldehyde)

    3. CH3CH2CH2CH2OH + CH3COOH CH3COOCH2CH2CH2CH3 (esterification)

  • Tetsu ChemSenseprovided a better condition in doing the step 3 for Fu's answer:


  • Fu said thanks to Tetsu:唔該你提醒我,我成日都唔記得.

  • Tetsu's reply to his thanks: You are welcome. Actually your answer is very reasonable ka. But if you can add the complete condition to it, it will become perfect ~!

Students attitude towards collaborative learning
Students’ attitude towards collaborative learning ChemSense

  • Most students showed positive attitudes towards collaborative learning.

  • Students encountered less difficulty when learning organic chemistry collaboratively.

  • Prefer face-to-face communication than learning through computers

  • High ability students preferred to work individually.

Attitude towards chemsense in supporting collaborative learning
Attitude towards ChemSense in supporting collaborative learning

  • Students were positive regarding using ChemSense to support collaborative learning.

  • Students were dissatisfied with the drawing tools of ChemSense.

  • Students did not see the support of asynchronous communication in ChemSense as a means to help overcome the problems of time and space.

References learning

  • Bojczuk, M., Topic difficulties in O-and A-level chemistry, S.S.R., 1982, 224, 63, 545-51

  • Brook, W., The teaching of organic chemistry in schools – can we learn from the Kenyan experience? S.S.R., 1988, 69, 575-578

  • Furniss, B.S. and J.R. Parsonage, Organic chemistry as an A-level topic, S.S.R.,1977, 206, 59, 132-77

  • Schank, P., & Kozma, R. (in press). Learning Chemistry Through the Use of a Representation-Based Knowledge Building Environment. Journal of Educational Multimedia and Hypermedia.

References learning

  • Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). "Analysis of a global online debate and the development of an Interaction Analysis Model for examining social construction of knowledge in computer conferencing". Journal of Educational Computing Research, 17(4), 397-431.

  • Henri, F. (1992). "Computer conferencing and content analysis". In A.R. Kaye(Eds.), Collaborative learning through computer conferencing: The Najaden papers (pp. 115-136). New York: Springer.