A simulator for explaining organic chemical reactions using qualitative ...

Education in a technological world: communicating current and emerging research and technological efforts _______________________________________________________________________________________ A. Méndez-Vilas (Ed.)

A simulator for explaining organic chemical reactions using qualitative reasoning approach Alicia Y.C. Tang College of Information Technology, University of Tenaga Nasional, Selangor, Malaysia In science education, it is believed that students should understand the qualitative principles that govern the subject including the cause-effect relationships in processes before they are immersed in complex problem solving. When these fundamental skills are acquired, the entire learning activity can be made more effective. Our past survey showed that a large number of chemistry students had difficulty in understanding organic chemical reactions. They learn the subject by memorizing the basic facts, steps and formulas of each reaction which are easily forgotten. Traditional chemistry educational software is inadequate in promoting understanding such as why and how things happen. These programs do not “explain” simply because the results are obtained through chaining of rules or by searching the reaction routes that have been pre-coded in software. This paper describes a simulator, named QRiOM that combines qualitative reasoning and Qualitative Process Theory (QPT) ontology in a problem-solving system, and generates explanations for learners from the problem-solving system. The modelling constructs of QPT supports the representation of chemical theories qualitatively with notions of causality which can be used to explain the behaviour of a chemical system. In this work, the chemical theories that are required to understand organic processes are represented as QPT qualitative models. We will show how a qualitative model can help articulate ideas about a learning task and to improve a learner’s reasoning ability. The major theme of this work is that, in a qualitative simulation environment, students are able to articulate his/her knowledge through the inspection of explanations generated by software. These students are seen as the recipients of knowledge delivered via the “explanation” pedagogy. From a student evaluation exercise, the tool has enhanced student knowledge in the subject where the chemistry students are observed to learn better in terms of their conceptual understanding of the chemical reactions. The protocol for interacting with the simulator and a detailed discussion of the simulation results will also be presented. Keywords: chemical theories; explanation; qualitative reasoning; simulation

1. Introduction In organic chemical reactions, one has to understand the many cognitive steps involved before a stable product is formed. Understanding these cognitive steps is among the many difficulties faced by chemistry students. Traditional chemistry educational software is inadequate in promoting understanding such as why and how things happen. These programs do not “explain” simply because results are obtained through chaining of rules or by searching the reaction routes that have been pre-coded in software. Qualitative Reasoning (QR) is an Artificial Intelligence (AI) technique that attempts to model behaviour of dynamic physical systems without having to include a bunch of chemical reaction formulas and quantitative data in the system. The qualitative reasoning approach, although no longer a new research field in Artificial Intelligence (AI), its exploration in chemistry domain remains widely open. QR is concerned with reasoning from the basic principles of a given domain. The nature of the chemistry domain described in this work is very qualitative [1], so it is a very suitable field for applying the qualitative reasoning approach. QR formalisms which make causality explicit are of value in education [2]. An overview of QR research has been discussed in [3] while an analysis of QR in education can be found in [4]. Systems based on qualitative reasoning are expected to possess the ability to predict and explain the behaviour of physical systems in qualitative terms without involving mathematical equations. Many application areas can benefit from QR approaches. There is nothing wrong with organic chemistry instruction, but the explanation in classroom lacks “reasoning” and “articulation” characteristics. This paper describes a simulator prototype, named Qualitative Reasoning in Organic Mechanisms (QRiOM) that predicts and explains the chemical behaviours of organic reactions based on Qualitative Process Theory (QPT) ontology [5]. The modelling constructs of QPT provide basis for representing chemical theories qualitatively with notions of causality which can be used to explain the behaviour of a chemical system. The major theme of this work is that, in a qualitative simulation environment, students are able to articulate his/her knowledge through the inspection of explanations generated by software. These students are seen as the recipients of knowledge delivered via the “explanation” pedagogy. The approach used in this work stresses on the causal theories, in which the behavioural aspect of the problem is described in qualitative terms, such that the “causality” concept is inherent in the process model. Causality can be used to manifest order upon the world. For example, when given “X causes Y”, we believe that if we want to obtain Y we would create X. As such, when we observe Y we will think that X might be the reason for it. This new representation and explanation approach, when applied to reaction simulation through qualitative reasoning can help enhance learners’ reasoning ability.

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2. Functional components of the qualitative simulator This work investigates the combined use of QR approach and QPT ontology for the development of QRiOM tool for learning organic chemical reactions. The relationship between simulation, reasoning, and explanation is depicted in Fig. 1. Figure 2 shows the functional components implemented in QRiOM. The main components are: (1) Substrate Recognizer (for checking user selection), (2) Model Constructor, (3) Reasoning Engine (performing the actual simulation), (5) Explanation Generator, (6) Causal Model Generator (for constructing a causal graph to produce accounts of behaviour), (7) Molecule Update Routine (for keeping track of the structural change of the substrate), (8) Process Models, (9) Knowledge Validation Routine, (10) OntoRM ontology (for defining chemical knowledge relating to the “mechanism” of a reaction), and (11) Chemical Knowledge Base (for storing information such as chemical facts and chemical theories in QPT terms).

Various Types of Explanation

Qualitative Simulation

Behaviour Prediction

1. 2. 3.

Reasoning about the behaviour of the model

6

Causal Model Generator

9

4 Simulated Results (Final products and the mechanism used)

Molecule Patterns Storage

Causal changes (that stem from QPT process reasoning) Qualitative states of all parameters A piece of “history” of processes that occurred in a simulation

5

Explanation Generator

7

Molecule Update Routine (MUR)

Knowledge Validation Routine

3 10 OntoRM

Qualitative Simulator

(Reasoning engine)

QSA

Causal Reasoning (The study of the cause-effect interaction among parameters)

8

QPT Process Models

Explanation

Qualitative Model Constructor

2

Substrate Recognizer

11

Chemical Knowledge Base 1

Graphical User Interface

Fig. 1 The use of qualitative reasoning, simulation and explanation within the context of this work.

0

Fig. 2 Functional components in the QRiOM simulator.

2.1 Thought processes for a chemical equation Before we describe how a qualitative model can help articulate ideas about a learning task, we will first describe the chemical theories and behaviour of an organic chemical reaction. An organic reaction is a chemical reaction involving organic compounds, usually between an electrophilic centre and a nucleophilic centre. In any chemical reaction, some bonds are broken and new bonds are made. Atoms can form bonds by sharing unpaired electrons (also called “lone pair electrons”). Often, these changes are too complicated to happen in one simple stage. Thus, usually a reaction may involve a series of small changes one after the other. The chemical equation “(CH3)3COH + HCl  (CH3)3CCl + H2O” describes a functional group transformation reaction, where nucleophilic substitution (halogen substitution) is the mechanism for obtaining the final product. The series of the small reaction steps involved in converting the starting material ((CH3)3COH, a tertiary alcohol) to the final product ((CH3)3CCl, alkyl halide) is depicted in Fig. 3. Table 1 gives the names of each chemical reaction and the reactants used in the chemical processes. Table 1 Reactants and their associated chemical processes. Name of the chemical process Reaction step 1: Protonation (“make-bond”)

Reactant 1 (CH3)3COH (nucleophile)

Reactant 2 H+ (electrophile) (CH3)3C–OH2+

(CH3)3C+ (electrophile)

Cl (nucleophile)

Reaction step 2: Dissociation (“break-bond”) Reaction step 3: Capturing of anion by carbocation (“make-bond”)

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O =nucleophilic centre H+=electrophile

.. (CH3)3C – O: |

.. + H – Cl: ..

..+ (CH3)3C–O–H |



H

.. + :Cl: ..

H

tert-butyl alcohol

hydrogen chloride

tert-butyloxonium ion

chloride ion

(a) Reaction step 1

C = + O = -

..+ (CH3)3C– O–H |

(CH3)3C+



+

H

.. :O–H | H

tert-butyloxonium

tert-butyl cation

water

(b) Reaction step 2

C+ = electrophilic centre

Cl= nucleophile

.. (CH3)3C

+

+

tert-butyl cation

:Cl: ..

-

.. 

chloride ion

(CH3)3C–Cl: .. tert-butyl chloride

(c) Reaction step 3 Fig. 3 The conversion of a tertiary alcohol to yield alkyl chloride can be described as a series of three small steps.

2.2 Predicting “A Reacts to B” using qualitative reasoning The QPT’s qualitative proportionalities and influences are powerful primitives to be used in chemical reaction modelling in building chains of causality. In this work, the outcome of “A reacts to B” is predicted by performing qualitative reasoning based on the qualitative models constructed for the organic chemical processes. An organic reaction usually takes place between a nucleophile (electron-rich species) and an electrophile (electron-deficient species). A QPT model (Fig. 4) is used to describe a chemical reaction that adds a covalent bond between a nucleophile and an electrophile. The QPT model in Fig. 4 captures the chemical theories of the “protonation” process. The generic name for such an organic process is termed as “make-bond”. In Fig. 4, you may read the right column as “If (A and B) Then (C and D)”. In this case, C and D are qualitatively reasoned. The notion of “processes” defined in [4] is used as the main cause of change in a chemical system. This process occurs when the individuals (a nucleophile and an electrophile) are available. It is indicated in the “quantityconditions” slot. We represent chemical changes as starting from the direct influence (I+/I-) which then propagates via indirect influences (the “P”, stands for “proportionality”). Influences contain statements that specify what can cause a quantity (parameter) to change, through direct influences imposed by the process (label C). As the process occurs, bond-activity is a direct influence’s quantity and it has a positive influence (I+) on the no-of-bond, which is defined as two direct influence statements using the “I+/I-” notation of the QPT. Other propagation of effect is defined in “Relation” slot (label D). It is propagated via a set of qualitative proportionalities defined in the QPT process model. Each slot in the model will be manifested in Section 3. Algorithms determine what the behaviour is, not an explanation of it. An explanation of system behaviour may take many forms. An example is “causality” or “causal accounts”. Causal account is a kind of explanation that is consistent with our intuitions of how systems function. Since one of the objectives of this work is to prepare and generate explanations in a language and format understandable to the learner, causal graphs (also called state diagrams) are used to explain and justify solutions that are returned by the simulator. The issue of lack of explanation in chemistry software is addressed by embedding the so-called “causal explanation generator” in the simulator. The generator justifies and explains a simulated result by tracing the chains of causality that stem from QPT model reasoning. These features are demonstrated via QRiOM. We will discuss in Section 3 how the QPT models can be used to explicate chemical phenomena as a means for supporting students’ articulation skills in learning organic chemical reactions.

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Process Slots Individuals

Modelling constructs in QPT 1. H ;represents hydrogen 2. O ;represents the alcohol oxygen 3. Am[lone-pair-electron(O)] >= ONE 4. charges(H, positive) 5. electrophile(H, charged) 6. nucleophile(O, neutral) 7. charges(O, neutral) 8. I + (no-of-bond(O), Am[bond-activity]) 9. I + (no-of-bond(H), Am[bond-activity]) 10. DS [charge(H)] = -1 ;decreasing sign 11. DS [charge(O)] = 1 ;increasing sign

Quantity-Conditions

Direct Influences Relations

12. lone-pair-electron(O)

A B

C

P no-of-bond(O)

P lone-pair-electron(O) 14. lone-pair-electron(H) P no-of-bond(H)  15. charge(H) P no-of-bond(H)

13. charge(O)

D

Fig. 4 A “make-bond” model fragment represented using QPT. This model fragment is used to reproduce the behaviour of the first reaction step for “(CH3)3COH + HCl”.

2.3

Causal explanation

Dynamic explanation is one of the most challenging aspects in the design of a learning tool for teaching purposes. How can explanation be derived from the qualitative reasoning? An approach that derives explanation from causal graph based on QPT is proposed and implemented. A causal graph depicts the set of causal relationships between quantities (chemical parameters) occurring in the simulation. The inspection of cause-effect chains can help learners pick up the underlying concept better than merely memorizing the reaction steps. QRiOM is able to provide this type of explanation on demand. Qualitative proportionality of QPT helps represent the propagation of effects of change caused by organic 

processes. As an example, two proportionalities are given as follows. Qprop1:“lone-pair-electron(O) P no-of

bond(O)”, and Qprop2: “charge(O) P lone-pair-electron (O)”. Qprop1 says “Increasing no-of-bond will cause a decrease in lone-pair-electron”, while Qprop2 is read as “Decreasing lone-pair-electron will cause an increase in charge”. Note that all the above descriptions are purely qualitative and symbolic. There is no need to include quantitative data in the prediction. Fig. 5 is a simplified version of a causal graph that explicitly represents the causeeffect notion in the “make-bond” process between a nucleophile (“O”) and an electrophile (“H+”). Note that, I = Influences (describing the direct effect caused by a process), and P = Proportionalities (describing relationship between two parameters). Effects are then propagated via the direct (I) and indirect (P) influences. The process’s quantity is bond-activity. This quantity directly influences no-of-bond for “O” and “H”. In other words, after the protonation process “O” will have an extra covalent bond. The effects will propagate to other dependent quantities as shown in the diagram. We perceive this type of explanation to be more natural and dynamic (runtime generation). Fig. 5 A fragment of the causal graph for the “make-bond” process. The inequality statement shown above the dotted line represents the quantity-condition that must be true for the process to start.

lone-pair-electron(O) >= min-electron-pair(O) protonation-activity I+ no-of-bond(O)

P lone-pair-electron(O)

I+

bond-activity(O)

Icharge(H)

P no-of-bond(H)

P charge(O)

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3. Learning with qualitative models If a student learns what is behind the equation “AB + CD  AD + CB” as “it is only an exchange of nucleophile, hence it is just a futile problem”, or simply memorize “AB + CD” will give rise to “AD + CB” then the learner would not be able to answer basic questions such as: Why did the process occur? What is favourable in the step? Why a bond is made at the particular atom and not at the other atom? The QR approach provides a systematic way for converting and solving the chemical equations so that students would know why a particular process is taking place and how a particular outcome is produced. We believe this approach of modelling organic reactions will help improve a learner’s reasoning ability. An increasing level of conceptual understanding about the subject can also be expected. 3.1 Ontology primitives as explanation facilitator With a chemical reaction, we know what to start off and after qualitative simulation we get what it finishes with (the final product), but to understand the reaction we want to know the story in between and this is called “mechanism”. An organic mechanism is normally used to explain how a product is formed. Equation (1) will be used to show how the general behaviour of organic reactions can be explained. (CH3)3COH + HCl  (CH3)3CCl + H2O

(1)

Model inspection can help sharpen a learner’s reasoning ability in the way that the learner has to think hard why the statements in each slot of a given QPT model are relevant or negligible. The model inspection activity is divided into a series of learning tasks. All learning tasks are based on the “protonation” process as illustrated in Fig. 4. During a reaction simulation, several types of queries may be expected. From an interview conducted during the knowledge acquisition stage, the most popular questions the students would ask are:  What are the reacting species (the “individuals” in QPT terms) used in the chemical process that occurred? Refer to learning task 1 for the answer.  Will a covalent bond be added or deleted from the compound? Refer to learning task 2 for the answer.  What happens to the functional group? Refer to learning task 3 for the answer.  Why did the process occur? Refer to learning task 4 for the answer.  Why was the process stopped? Refer to learning task 5 for the answer. 3.2

Learning tasks manifestation

Note that line numbers are based on the enumeration used in Fig. 4 (the QPT model for the first reaction step of “(CH3)3COH” reacts to “HCl”).  Learning task 1: Proton (H+) and alcohol oxygen (OH) are needed to start the reaction simulation. Learners would be able to find this by inspecting the “Individuals” slot. Briefly, the slot indicates that, in order to begin the first step of Eq. (1) simulation, a proton is needed which serves as an electrophile together with a species which has a nucleophilic centre. In this case, the nucleophilic centre is the “O” from the “OH” group (termed as alcohol oxygen) which has lone-pair electrons to be donated. Line 1 and Line 2 show exactly the existence of hydrogen ion together with the “OH” functional group from the alcohol which help explain why the two substances are required.  Learning task 2: In Line 3, the inequality (lone-pair-electron >= ONE) for the “O” (which is the nucleophilic centre of the alcohol substrate) says “there is at least one lone pair of electrons to be donated to H+”. Lines 4 – 5 indicate that “H” is a charged species and thus it will act as an electrophile in the reaction. As such a covalent bond will be added to the compound (the alcohol substrate).  Learning task 3: When the “make-bond” process begins, the “O” will have an extra covalent bond while the “H” will be neutralized. Chemistry students can appreciate such concept by examining the functional dependencies as defined in Lines 12–15.  Learning task 4: The process occurred because the statements in quantity-conditions (Lines 3 – 7) are satisfied, which states that “alcohol oxygen with at least one lone pair of electrons is needed so that the electrons can be donated to the proton in order to make a bond”.  Learning task 5: Lines 12–15 are manifested as follows: When the process begins, the “O” will have an extra covalent bond while “H” will be neutralized. When more covalent bonds are made on “O”, its number of lone pair electrons will decrease via the inverse qualitative proportionality. When the lone-pair-electron on “O” decreases the charge on “O” will increase. These relationships explain how the “O” donates a pair of electrons in order to form a bond. At this point of time, the quantity-condition has been violated. Therefore, the process is deemed to stop.

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4. The protocol for interacting with QRiOM Figure 6 shows the problem solving model of QRiOM. In this work, the tool is designed in such a way that the user is taken through the modelling, simulation and explanation pages step-by-step. A computer screenshot of the main interface of QRiOM is given in Fig. 7. Select substrate and reagent

Corresponds to H

Study changes in atoms’ chemical parameters

B

Build process model (Automate QPT model construction)

Corresponds to H

C Corresponds to B

Analyze causal graphs in explanation page

D

Run simulation

View final products and the reaction mechanism used

Inspect qualitative model (QPT models)

Corresponds to G

A

Corresponds to A

Examine the entire reaction route

E

Corresponds to C, D & E

F

G

H

I

Corresponds to F

Fig. 6 Protocol in using the simulator (Labels A – H can be found in Fig. 7).

Fig. 7 Main interface of the QRiOM software.

5. Simulation results and discussion As it is claimed, all the outputs are produced dynamically. There is no pre-coded reaction route in the program as in the traditional software development approach of chemistry educational programs. Table 2 gives a summary of the computer screenshots together with the objectives they served. At the end of a simulation, QRiOM returns the final products formed, as well as the following simulation results and explanations:  The entire reaction route of a qualitative simulation. Such an output permits learners to study how a substrate’s molecular pattern is changed from one reaction to another (Fig. 8).  The qualitative model used for predicting the behaviour of a chemical reaction (Fig. 9).  A causal graph that depicts the reacting species used, the intermediates produced, and the cause-effect chain of chemical parameters in the simulation (Fig. 10).  The whole set of the parameter state histories assigned to each quantity in the reaction (Fig. 11).  The atom property table that contains the chemical states of each reacting unit involved in the reaction (Fig. 12). Table 2 Computer screenshots and the respective educational objectives. Computer screenshots Reaction route of a qualitative simulation (Fig. 8)

Educational objectives  Promote conceptual understanding  Promote ability to articulate various aspects of a reaction

QPT model for organic processes (Fig. 9)

 Promote ability to articulate various aspects of a reaction

Causal graph (Fig. 10)

 Promote conceptual understanding  Promote ability to articulate various aspects of a reaction

Parameter state histories (Fig. 11)



Promote conceptual understanding

Atom property table (Fig. 12)



Promote conceptual understanding

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Reaction route: In QRiOM, a substrate’s structural change is represented in 2D format, resulting in the so-called “reaction route”. The reaction route gives the step-by-step change of the molecular structure of an organic substrate. An example is depicted in Fig. 8. QPT model: Students typically have problems in describing the chemical parameters needed to solve the problem. This is due to lack of the necessary chemical intuition, especially on how to relate the parameters within a situation. When inspecting a model, students have to articulate relationships between entities and dependencies. This can help improve their reasoning ability. A screenshot of model inspection page is shown in Fig. 9.

Fig. 8 Step-by-step change of the molecular structure of an organic substrate.

Fig. 9 A computer generated QPT model.

Causal graph: Much of the explanation used by QRiOM is achieved by tracing the effect propagation through the modelling constructs of QPT. For example, during each reaction simulation, a causal graph (Fig. 10) is generated that shows the use of the qualitative proportionality statements in the QPT models. Causal models help learners to rationalize why a particular process occurred. This can lead to a deeper understanding of chemical processes.

Fig. 10 A causal graph generated by QRiOM that enables learners to examine the cause-effect relationships of chemical parameters during reasoning.

Fig. 11 The states of chemical parameter of each reacting species involved in a simulation task can be examined in greater detail.

Parameter state history and atom property tables: Learners can also browse the behavioural change of parameters belonging to each reacting units (Fig. 11) in which all the reacting units used in the simulation is populated to a pulldown list. The values assigned to the chemical parameters during simulation are recorded in special-purpose data structures for future retrieval. One such structure is the atom property table (Fig. 12a). These results can then be used to generate the necessary reaction route (Fig. 12b). The structure of the final product can be easily drawn from Fig. 12a. For example, when the charge on “C” is positive (A1, Fig. 12a), then a positive sign is assigned next to the “C” atom (B1, Fig. 12b). Likewise, at A2 of Fig. 12a (under “After step 3” heading), the “C’ regained its stability and this change is reflected at B2 of Fig. 12b.

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A1 B1

A2

B2

(b) (a) Fig. 12 (a) The chemical states possessed by each reacting unit during simulation are stored in the atom property table, (b) A reaction route drawn from using the data values in the atom property table.

Since QRiOM is developed to promote learners’ understanding of chemical reactions, the effectiveness of QRiOM in explaining organic chemical phenomena has also been evaluated. The setup included paper-based pre- and postassessments concerning their skills in a few core areas of the subject. A questionnaire was used to gauge the participants’ responses about QRiOM in terms of the helpfulness and usefulness of the explanation generated by the software. Evaluation results show that the tool has enhanced student knowledge in organic chemical reactions [6]. The results of the evaluation suggest that QRiOM is effective in terms of its ability to promote understanding in learning organic processes through the inspection of the various forms of explanation generated by the tool.

6. Future work and conclusion This paper describes our research that combines qualitative reasoning and ontology in a simulation-based system, and generates explanations for learners from the simulation-based system. The first contribution is the application of QPT to model various organic chemical reactions and to reproduce the chemical behaviour “intuitively”. The work also provides justifications that QPT can be effectively used to support learning. The second contribution is the development of an explanation module obtained from the process model directly. After developing and testing the simulation prototype, we anticipate a fully usable system that can assist chemistry students not only in understanding the subject, but also engaging them in building simple models as a means to acquire knowledge.

References [1] [2] [3] [4] [5] [6]

Tang AYC, Syed Mustapha SMFD, Abdullah R, Zain SM, Rahman NA. Towards automating QPT model construction for reaction mechanism simulation. In: Price C, Snooke N, eds., The 21st International Workshop on Qualitative Reasoning, Aberystwyth, United Kingdom; 2007. Salles PSBA, Pain H, Muetzelfeldt RI. Qualitative Ecological Models for Tutoring Systems: A Comparative Study. AAAI Technical Report WS-96-01; 1996. Bredeweg B, Struss P. Current Topics in Qualitative Reasoning. AI Magazine (special issue), 2003; 24(4), 13–130. Bredeweg B, Forbus KD. Qualitative modelling in education. AI Magazine, 2003; 24(4): 35–46. Forbus KD. Qualitative process theory. Artificial Intelligence, 1984; 24:85-168. Tang AYC, Zain SM, Abdullah R. Development and evaluation of chemistry educational software for learning organic reactions using qualitative reasoning. Intl. J. of Education and Information Technologies, 2010; 3(4): 129-138.

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