QALSIC: Building an Articulate Educational Software forHigh School Inorganic Chemistry Laboratories

S.M.F.D Syed Mustapha, J.S. Pang, Department of Artificial Intelligence, University ofMalaya, 50603, Kuala Lumpur, Malaysiasmfd@um.edu.my,pjs@mmm-technology.com

S.M Zain, Department of Chemistry, University of Malaya, 50603, Kuala Lumpur, Malaysiasmzain@um.edu.my

Abstract. In building an intelligent tutoring system, researchers quest for robust techniques and suitableapproaches so that intelligent and interactive communications between learners and the system can beseamlessly established. The aspects of communication encompass the four properties of articulate softwaredefined by Forbus (2001) which are fluent, supportive, generative and customizable. The building of articulatesoftware (QALSIC) for learning inorganic chemistry at high school level is discussed in this paper. The fourproperties are represented in our system through knowledge-based and qualitative process theory approaches.The interactiveness and intelligence of the system are demonstrated in its intelligent responses to unprecedentedevents, unknown substances, and flexibility in usage. The system can be used symbiotically with the traditionallaboratory, as well as, as an independent system in which it offers a more flexible learning environment at alower cost. The strength of the system is centred at its explanatory capabilities in using chemistry knowledge.We envisage that the system will improve intuitive learning in students while enhancing the traditional didacticlearning.

Keywords. Interactive Learning Environments, Improving Classroom Teaching, Intelligent TutoringSystems

INTRODUCTION

We believe that interactiveness and intelligence are two concomitant factors that can be coalesced inthe development of educational software. Development of interactive educational software has beenthe interest of many researchers and has become more pervasive with the introduction of multimediatechnology (Miller, 1993). On the one hand, interactivity in the usual sense requires the software to beaccompanied with graphics, sound and animated pictures, as well as text. Our definition ofinteractiveness, on the other hand, emphasizes making the system articulate the knowledge itcontains, infer new facts, or generate new recommendations from the basic principles, and to performreasoning in deriving explanations such that the communication between the learners and the systemis intelligent-like and knowledge-driven. For this to happen, in this project, we address the four issuessuggested by Forbus (2000) in which an articulate software should be:

i. fluent: having some understanding and the ability to communicate knowledge it contains,ii. supportive: having the components of coaching and tutoring to assist students in problem

solving,iii. generative: allowing students and instructors to pose new problems rather than deal with

pre-stored information or questions; andiv. customizable: allowing instructors to modify, update and extend the libraries of

phenomena, designs and domain theories used by the software without the need to mend atthe programming level.

The theory mentioned above has been adopted successfully in the development of articulatesoftware in the field of thermodynamic (Forbus, 2000). A case study has been made (Baher, 1998a;Baher, 1998b) to evaluate the effectiveness of the software in teaching and learning. We hypothesizethat this theory can be assimilated into educational software of other science domains, in particular tothe laboratory environment of an inorganic chemistry class. QALSIC has been developed where thetheory has been demonstrated through its ability to capture the principle knowledge about chemicalproperties and reactions, to provide guidance through mix-initiative strategy, to respond by giving anarrative explanation to any possible experimental scenario posed by the students, and to allowalteration and addition of chemical facts and principles by the teachers. The chemical facts are kept inthe knowledge-base while the reasoning is handled by qualitative process theory.

By the adoption of the theory, QALSIC provides flexibility in learning in terms of providingextended experimental hours through its laboratory simulator and widens experimental opportunitiesby allowing students to set up their own testing sequences. The learning pedagogy can subsequentlybe expanded from didactic learning to inquiry-based learning (Syed Mustapha & Daniel, 2003).Students have the opportunity to test their preconceived beliefs of some chemical reactions even withthose outside of what have been prescribed by the experimental workbook in the traditionallaboratory (throughout the text, wet laboratory will be used interchangeably).

In the next section we discuss the current problems that arise in learning chemistry via thetraditional laboratory. In the same section, we also discuss the possibility of synergizing thetraditional laboratory and the simulated environment.

PROBLEMS OF LEARNING CHEMISTRY

In a traditional high school inorganic chemistry laboratory, the students are required to determine theconstituents of a given substance i.e. the cations and anions as well as to establish the chemicalformula of the substance. The two levels of analysis involved in this process are the qualitativeanalysis and the quantitative analysis. The qualitative analysis is when one plans a set of relevantexperiments and orders the testing sequence. In the quantitative analysis, one needs to determinewhich ions are present in the substance and to formulate its name. Students are required to have somebasic chemical intuition before they can make a good guess as to which experiment is right to beginwith. The types of chemical substances are within the scopes defined in the periodic table commonlyused by most laboratories in high schools worldwide. However, most average students do not fullycomprehend the basic knowledge of chemistry, yet they are supplied with the workbook which statesthe experimental steps to follow. If the observations deviate from the expected ones, the students areinstructed to redo the experiment as it is regarded as a failure. This approach of teaching leads to

drawbacks in the student's learning behaviour because students are not given opportunities to look foralternatives in deducing the potential ions. Thus, students are not able to develop their own chemicalintuition and understanding in terms of relationships of the cause and effects in certain chemicalreactions. Furthermore, students are not given opportunities to use their intuitive chemicalunderstanding to explain the chemical reactions they observe. Even though the students are able tocomplete the sequence of experiments and deduce the right ions, it does not guarantee that theyunderstand why certain tests are chosen in that order and what would happen if the order wererearranged. Finally, they are not given flexible access to a laboratory environment for furtherexploration. Perhaps, these restrictions are acceptable for safety reasons but it is at the expense oflearning opportunities. It is thus safe to say that the current laboratory learning does not cultivate instudents the power of empirical reasoning. QALSIC, we believe may be able to solve this problem. Inthe following subsections we describe how traditional laboratory learning can be extended by usingQALSIC.

EXTENDING THE TRADITIONAL CHEMISTRY LABORATORY

In this subsection, we illustrate in theory how QALSIC is used in the actual learning environment. Inthe traditional laboratory, students are given an unknown substance by the teacher and they arerequested to determine its constituents. The students perform the experiments and record theirobservations in the workbook. We simulate this working environment in the QALSIC system wherethe traditional laboratory can be included or excluded from the entire experimental scenario. Theformer suggests that the QALSIC system is used while the actual experiment is done in the traditionallaboratory. On the other hand, the latter suggests that the entire experiment is conductedindependently from the traditional laboratory. Nevertheless, we do not claim at this stage that theintelligent system can substitute the entire functionalities of the traditional laboratory. There arecertain exposures such as the physical appearances of the substance and the live witnessing ofchemical reactions during the experiment that are crucial for the students. In order to synchronize andtake full advantage of both situations, our system can be used in both environments.

Figure 1 shows an example of a symbiotic environment where the system is used in the actuallaboratory. In this approach, the analysis planning, observation reporting and ion deduction are doneon the system as the system is equipped with common chemistry knowledge to assist the students.Comments and suggestions about the students' observations can be given by the system as needed.The students are given the flexibility to choose their own finding or yield to the system's analysis tobe included in their report. Since the experiment is conducted in an actual laboratory, the instructor isavailable for reference if unanticipated circumstances are encountered.

Figure 2 shows an entirely independent process of running the experiment on the system (i.e.without the wet laboratory and the instructor). The students switch back and forth between thequalitative analysis and qualitative simulation systems to complete the entire experiment. (InQALSIC, qualitative analysis is the component that allows students to plan the experiment, to reporton the observation and to make the deduction.) The qualitative simulation is a simulator thatgenerates a narrative text which explains the chemical reactions that has occurred. The detaileddescription of these two functions will be given in the subsequent subsections. These functions offerextra learning opportunities to the students who wish to redo the experiment in a different way fromthe ones they did in the school. Instructors are not needed as the system is capable of generating an

explanation for many situations including the unanticipated occurrences. The independent mode ofrunning an experiment described here can cut the experimental cost since the experiment can be donerepetitively without involving the actual substance and apparatus.

Qualitative Analysis

Student is given an unknown sample in the real laboratoryby the teacher

Student chooses the reagent and a series of tests to deducethe final composition

Student has the flexibility to decide the subsequent steps anddeductions or yield to the system's suggestion

Student performs analysis planning, makes observational reportand deduces potential ions on the system

Instructor

Student refers toinstructor for

unanticipated events

Wet laboratory

Student uses actuallaboratory to havereal experimental

experience

Fig. 1. Symbiotic environment in the wet laboratory.

Qualitative Analysis and Simulation

Student is given an unknown sample by the system

Student plans the series of tests and a set of reagents that may berelevant to the deduction of the right candidates of ions

Student runs the experiment using Qualitative Simulation system

Student has the flexibility to decide the subsequent tests and to makedeductions based on the personal understanding or yield to the system's

suggestion

Fig. 2. Independent experiments without the wet laboratory or instructor.

Student performs the analysis planning, makes an observational reportand deduces potential ions on the system

The two types of learning environment do not supersede one another but they do complementeach other. In the following section, we describe the QALSIC system and its architecture thatsupports the two types of learning environment as described in Figure 1 and Figure 2.

THE TECHNICAL ASPECTS AND PROCESS FLOW OF QALSIC

QALSIC (Qualitative Analysis and Laboratory Simulation for Inorganic Chemistry) was developed toassist chemistry students at the high school or pre-university levels. The two major components,namely the qualitative analysis and qualitative simulation, are developed based on differentapproaches. Qualitative analysis is developed using a knowledge-based system approach whilequalitative simulation uses the Qualitative Process Theory approach. The knowledge base consists ofrigid rules and chemistry facts. Among the earlier systems using this approach were CHEMPROF, anintelligent tutoring system for teaching chemistry (Eggert, Middlecamp & Jacob, 1992) and EXSYS(Settle, 1985), an expert system with an editor that allowed the knowledge engineer to alter rules inthe knowledge-base. Qualitative simulation is able to provide an explanation without the need toprecode each possible event. This technique is contrary to the ModelScience approach(ModelScience, 2003) in that the system limits a set of events which are allowed to be tested and doesnot permit tests that have not been pre-defined by the system. In QALSIC, the explanation isgenerated by examining the relationships, correspondences and influences defined in the qualitativeprocess theory.

Figure 3 shows the main architecture of the QALSIC system. The database has the chemicalfacts such as the element names, color, solubility products, quantity components, indirect and directinfluence components and correspondence components. Influences and correspondence componentsare the Qualitative Process Theory (thereafter, QPT) knowledge representational language which isessential in describing the interrelationships of objects in terms of the physical functionalities e.g.High concentration of A reduces the dissociation of BC. Process vocabularies are a compendium ofqualitative processes defined in the system. Qualitative processes describe the chemical processeswhich are common to the chemical processes in the inorganic chemistry. Readers are referred to theintroductory literature of QPT by Forbus (1984, 1996) for the notations adopted in the subsequentsections.

Since the technical aspects and the process flow of the qualitative analysis and qualitativesimulation are different, they are described individually in the two different subsections.

Qualitative Analysis

In the qualitative analysis module, the student is given a list of reagents to choose from the system.As in the traditional laboratory, experienced students will be able to determine a reagent that can dothe most elimination on irrelevant elements. Figure 4 shows that there are three types of tests whichare different in their functionalities. Students have to make intelligent choices on how and when touse these functions as each function is used for a different purposes. In the Main Test (Ujian Utama),students can choose a reagent for a particular experiment from a series of choices. Students are alsogiven a facility in the Special Test (Ujian Khas) for substances such as alcohol, acetaldehyde,propanone and others which are not testable using ordinary testing methods. Verification Test (Ujian

Pengesahan) reconfirms whether a certain specific ion is present, an assumption obtained fromperforming several tests in the Main Test (Ujian Utama).

Fig. 3. Main architecture of QALSIC.

The main difference between Main Test (Ujian Utama) and Special Test (Ujian Khas) is that theformer often leads to few possible ions that may be present, while the latter will only lead to oneconclusion. An example for this is when one adds aqueous ammonia solution to an unknown and thereaction yields a white precipitation which is insoluble in excessive ammonia. This observation leadsto three possibilities, Pb2+, Al3+ or/and Mg2+. In other words, the Main Test (Ujian Utama) narrowsthe range of possible ions in order to perform Specific Test (Ujian Khas) to determine the exact ionthat is present in the unknown. Specific test (Ujian Khas) describes the presence of a specific ion in anunknown. Observations obtained will only lead to one conclusion. For example, to determine thepresence of ion Pb2+, there are three specific tests that are available as shown in Table 1.

In Figure 4, aqueous ammonia (NH3) is chosen as a reagent (indicated on the right part of thescreen). When the student adds aqueous ammonia to the unknown, observations obtained from theexperiment are input into the dialog box. Observations may include the Formation of Precipitation(Pembentukan Mendakan), the Changes of Solution Color (Perubahan Warna Larutan), or N oChanges (Tiada Perubahan). Notice that the input control for Color Change in Precipitation(Mendakan Tukar Warna) and Solubility in excess of NH3 (Kelarutan dalam NH3) are disabledbecause the user has not input the observed color in the Precipitation Color (Warna mendakan) field.This function prevents inconsistent input from being entered into the system. The system will alsoprompt the user for illogical input, such as the change of color of precipitation from black to white (a

snapshot for this example is not included here). The system acts as a coach or tutor and interruptsonly when the student makes common sense mistakes. This helps the students to classify theobservations and to focus on a specific observation. Table 2 shows three possible deductions that thesystem will generate based on the input given by the students on the screen shown in Figure 4.

In Table 2, the second observation is read as "white color precipitation occurs and the unknowndoes not dissolve in excess ammonia, therefore ions Pb2+, Al3+, Mg2+ might present in the unknown".There are 12 common reagents available in the system and some of them are listed in Table 3.

Fig. 4. Main screen of Qualitative Analysis.

Table 1Specific test to determine the present of ion Pb2+

Test ObservationAdd a few drops of KI solution to the unknown andheat it under fire. Let the solution cool off.

Yellow color precipitation of PbI2 is observed, anddissolved when heating. Yellow color crystalobserved when solution reaches room temperature.

Add HCl to the unknown. Formation of white precipitation (PbCl2).Precipitation dissolved in hot water, and manifestedagain when it cools off.

Add H2SO4 aqueous to the unknown. Formation of white precipitation (PbSO4).Precipitation dissolved in the ammonium Ethanoatsolution.Table 2

White color precipitation and deduction for ammonia test

Precipitation color(Warna mendakan)

Solubility in excessof NH3 (Kelarutan

dalam NH3)

Color change inprecipitation

(Mendakan tukar warna)

Deduction(Deduksi)

White color precipitation Dissolved in excessammonia

not observed Zn2+ present

White color precipitation does not dissolve inexcess ammonia

not observed P b2+, A l3+,Mg2+ present

White color precipitation,color changes to chocolate

does not dissolve inexcess ammonia

color change tochocolate

Mn2+ present

Extra information is also provided by the system as a reminder for the student to record furtherobservation on the specific test. Figure 5 is an example where the system prompts the user with extrainformation which says that the "blue precipitation may dissolve in excess aqueous ammonia to forma deep blue color solution". In Figure 6, when the radio button Change of solution color (Perubahanwarna larutan) is checked, all input such as Precipitation Color (Warna Mendakan), Color Changein Precipitation (Mendakan Tukar Warna) and Solubility in excess of NH3 (Kelarutan Dalam NH3)are disabled, except the input control from (dari) and to (ke) on the right. The intelligent responsefrom the system is managed by the chemical rules in the knowledge base.

Figure 5 System prompts a reminder to the user

blue precipitation may dissolve in excess aqueous ammonia toform a deep blue color solution

Fig. 5. System prompts a reminder to the user.

Table 3Reagents for the main test

1. Ammonia aqueous solution NH4OH2. Argentum Nitrate solution AgNO3

3. Concentrated Sulphuric Acid H2SO4

4. Barium Chloride or Barium Nitrate BaCl2, BaNO3

5. Iron(III)Chloride Neutral FeCl3

6. Hydrogen Peroxide H2O2

In Figure 7, students are given the option to record their observation and deduction or toautomate the report generation. The left part of the window allows the students to enter their reportwhile the right portion of the screen generates the Observation (Pemerhatian), Deduction (Deduksi),Comments (Komen) and Suggestions (Cadangan) automatically when the respective button is clicked.The students can choose either to accept the Model Answer (Model Jawapan) which is given by thesystem as the final answer or enter their own findings. This mixed initiative approach allows thestudents to make their own decisions throughout the period of solving the problems. At the sametime, the system's suggestion can be used as a reference. The system captures the students' actions andgenerates a report of the deductions which may be printed out for submission.

Fig. 6. Some response functions are disabled.

Fig. 7. Answer script after the first observation.

The chemistry knowledge built into the system assists the students in choosing valid options. Forexample if a reagent such as aqueous silver nitrate (AgNO3) is chosen, the system will limit the itemsin the list box such that for Precipitation Color (Warna Mendakan), the only possible options areBrownish Red (coklat kemerahan), Black (hitam), Yellow (kuning), Light Yellow (kuning pucat) andWhite (putih). The pedagogical benefit of doing this is to make the students realize what the possibleresultant colors for a particular reagent will be. The knowledge in the qualitative analysis is written inthe form of chemistry facts and rules. Even though students are given a large space for "trial anderror", their actions are guided by some common sense theory in chemistry as discussed above. Inchemistry, beside facts and rigid rules, there are basic chemical principles which students have tolearn.

The nature of the substances in inorganic chemistry is such that their reactions in a chemistryexperiment can not be modeled or predicted merely based on their categories or classification in theperiodic tables. That means, it is not possible to form generic rules that will ascertain theconsequences of certain chemical reactions for a particular class of elements. Due to this situation, theorganization of the chemical knowledge in QALSIC is rather rigid such that the observations and thedeductions of the possible ions are specified to a particular element.

Reasoning by principle can be modeled using the QPT technique. In this system, QPT is used tomodel knowledge of chemistry in such a way that the system is able to handle unanticipated eventsand can predict reactions for which the substance is new or unknown in terms of its characteristics.While qualitative analysis uses rigid facts and rules, the qualitative simulation can be modeled usingsome principles. In the next subsection, we demonstrate the capability of QPT in giving explanationand reasoning for anomalous behavior.

System auto-generates the observation

Student types in his observation here

Student can enter the possible cation or anionhere

System auto-generates thepossible ions that are present

Qualitative Simulation

Qualitative simulation replaces most of the parts in the actual activities in the laboratory. The mainfocus of qualitative simulation is to provide a chemical explanation of why certain reactions occur. InQALSIC, we emphasize the intelligence of reasoning and explanation capabilities of the systemrather than focus on graphical animations, as the programming work for the latter is secondary. Oneof the main characteristics of an articulate software is that it must be able to handle new situations orunprecedented events. An example of how an unprecedented event could occur is when a student failsto follow the procedure in the workbook correctly. Most of the current software such as ChemTutor(http://www.highergrades.com/) and Model Science (http://modelscience.com/), do not allow users toset experiments in which the results are unpredicted or unusual. They do these by limiting the optionsof certain chemical substances or experimental procedures. Unlike other software in the market, oursystem allows the students to choose a larger range of chemical substances and at the same time theyare able to predict possible reactions.

In order to describe further on the process flow of the qualitative simulation, we explain the twomodules that are required to support the underlying operations, namely, the simulation database andthe electron configuration engine.

QPT Database

QALSIC allows flexibility in maintaining the knowledge such that the students or teachers can add ordelete the qualitative processes in the database. As shown in Figure 8, the users (teachers andstudents) can define the process, quantity, qualitative proportionality and correspondence which will

Fig. 8. Simulation database.

be used in establishing relations and influences in the individual views and process views. Theexample given by Figure 8 defines the dissociation process which has dissociation rate as its quantity.Another quantity which is related to dissociation process is the concentration. Thus, the dissociationprocess is determined by the dissociation rate and concentration. The relation of these two qualitativeterms is specified in terms of qualitative proportionality where negative (-) influence describes theincrease of dissociation rate as the amount of concentration decreases. Correspondence definesrelationships in the limit points of the quantity space. More qualitative processes can be added asneeded. Examples of qualitative influences are given in Table 4.

Electron Configuration Engine (ECE)

ECE is another component in QALSIC that uses QPT in determining the valence electron. The mostbasic property of an ion is the atomic number, for example, Zn (zinc) has the atomic number of 30.Figure 9 illustrates the basic component of an ion in a knowledge base where only the most basicproperty is needed. The electron configuration engine assigns 30 electrons of zinc into its appropriateorbitals according to the two chemistry basic rules and principle, Hund's rules and Pauli's principle.Then the engine generates the written format of the electron configuration, for example, [1s2, 2s2, 2p6,3s2, 3p6, 3d10, 4s2] that represents the electron configuration of zinc. The placement of the electronresults in filling the outermost orbital, 4s2. The component of indirect and direct influences (describedby QPT theory) may be required in order to explain why zinc, when turned into an ion, has thetendency to lose 2 electrons.

Referring to Figure 9, the formation ion Zn2+ was the result of some processes that changes zincto zinc ion Zn2+. Table 5 has defined that Ionization process removes the most loosely held electronfrom an atom. As the process occurs, since the process is the only source of direct influence, thequantity that is directly influenced is the Oxidation number. A symbol that represents this process isI(Q,n), where n has direct influence on the quantity Q or precisely, it can be written as

I+(oxidation-number(Zn),A[ionization-energy])

Referring to the previous Table 4, ionization energy is indirectly influenced by atomic radii, whichis written as Ionization Energy αQ- Atomic Radii, which means, the bigger the atomic radii is, thelesser the ionization energy is needed to eliminate the electron situated at that position. Thus, fromthis fact, we can deduce that the principle quantum number (n) increases monotonically in itsdependence to the atomic radii (principle quantum number (n) αQ+ atomic radii). Therefore, wecan conclude that less ionization energy is needed for the removal of the outer electron of an atom,represented by the biggest principle quantum number (n).

At this stage, we can conclude that the removal of electrons will start at the outer orbital, but wehave no idea of how many electrons will be removed. Previously, Table 4 states that, ionizationenergy αQ± Stability of Electron Configuration, (± represents unspecified relationship). Matter withfully filled electron configuration is more stable than partially filled electron configuration. Therefore,we need additional information to disambiguate the situation and the information needed is domain-dependent information. The domain-dependent information, which is embedded in the reasoning,mentions that the maximum placement of electron in outer orbital of zinc, 4s2 is 2. Removing oneelectron will cause the zinc ion to be unstable, but removing the two electrons will stabilize the

electron configuration because the next outer orbital 3d10 is then fully filled with electrons. Thisexplains why ion Zn2+ with the oxidation number +2 is formed.

Table 4Indirect influences between quantities in the domain of inorganic chemistry

Quantity 1 Quantity 2αQ+ Principal quantum number (n)Atomic RadiiαQ- No. of proton in nucleus

Atomic Volume αQ+ Atomic RadiiαQ+ Atomic WeightAtomic DensityαQ+ Atomic RadiiαQ Types of metal bonding, Covalent, van der WaalsαQ+ Molecular arrangementαQ+ Number of atom/molecule in each molecular arrangement

Melting Point

αQ+ Number of electrons involved in bonding formationαQ Types of metal bonding, Covalent, van der WaalsαQ+ Molecular arrangementαQ+ Number of atom/molecule in each molecular arrangement

Boiling point

αQ+ Number of electrons involved in bonding formationConduction αQ+ Metal liked

αQ- Atomic radiiαQ+ Nucleus chargeαQ Stability in Electron ConfigurationαQ Types of sub-orbital electron

Ionization Energy

αQ- Shielding effectαQ ElectropositivityαQ ElectronegativityαQ- Atomic RadiiαQ+ Matter charge

Election Affinity

αQ Stability of Electron ConfigurationαQ- Atomic RadiiElectronegativityαQ+ Oxidation AbilityαQ- Net ChargeIonic RadiiαQ+ Principal Quantum number, n

Van der Waals Bonding αQ+ Number of atom in a moleculeαQ- Ionization EnergyReduction powerαQ+ Hydration Energy

There are five subprocesses involved in the entire process of qualitative simulation. Thesubprocesses are:

1. Recognizing agent2. Electron configuration (refer to section Electron Configuration Engine(ECE))

and constructing the molecular formula3. Constructing the individual views

4. Activating the process in the process vocabulary; and5. Constructing chemical equation

Fig. 9. The process of assigning electrons into an element's orbital according to chemistry theories specified bydirect and indirect influences.

For the purpose of specific illustration, the equilibrium state problem is chosen in this section(the explanation about equilibrium state is given in the next subsection). The process begins with theuser entering two reactants, for example, H2S and Fe. There are three possible mediums where theuser will choose one for the chemical reaction and they are the alkaline, water or acid. The name ofthe reactants are entered using computable format such as (H)2(S) for H2S, (CH3COO)(H) f o rCH3COOH and (Fe) for Fe. Using the Electron Configuration Engine (ECE), the number of electronscan be determined automatically. For example, if Fe is entered, the system will return Fe2+ where twoelectrons are removed.

Table 5Process vocabulary of chemical processes in inorganic chemistry

Quantity DescriptionOxidation Oxidation is the portion of the redox reaction in which there is a loss

of electrons by species or an increase in the oxidation number of anatom.

of electrons by species or an increase in the oxidation number of anatom.

Reduction Reduction is the part of a reaction in which there is a gain ofelectrons by species or decrease in the oxidation number of an atom.

Redox Reaction An oxidation-reduction reaction (redox reaction) is a reaction inwhich electrons are transferred between species or in which atomschange oxidation numbers.

Hydrolysis Chemical reaction of a compound with water, usually resulting in theformation of one or more new compounds.

Hydrogenation Adding hydrogen atom to another chemical substance.Halogenation Adding halogen atom to another chemical substance.Ionization A process to remove the most loosely held electron from an atom

Individual Views

Individual views (IV) are constructed automatically by the system. Individual views in Figure 10reflect the real situation in modelling the chemical reaction between Fe2+ ion and H2S, in an acidiccondition. The recognizing agent determines the objects that exist in the system, the state, and theobject's originality. Individuals are the collection of the objects. Preconditions contain conditions thatwill not change over time. Fe2+ ion, H2S and H+ from acid must dissolve in an aqueous solution at alltimes. Ion and aqueous solutions have the quantity type of concentration. Concentration may varyover time, but it must always be more than ZERO. Relations state the ions Fe2+ ion, H2S and H+

location and indicate that all ions have surface contact, which is the prerequisite factor for a reactionto occur. Ferum can exist in two different states, as in ion state or as a solid, shown in Figure 10(b),the same goes for Sulfur in Figure 10(c). The particular piece cannot be in both states at one time. Nostate description is given for H+ ion since it is impossible for H+ ion to exist as solid or liquidindividually under normal conditions (~ denotes "not"). When all the requirements of individuals,preconditions and quantity conditions are fulfilled, then the IVs of Figure10 should set to active.

Process Vocabulary

Processes start and stop when ordering of the quantity space change. In the quantity space ofconcentration, there exist two elements [ZERO, saturated] as landmark values. When solution H2S isadded, it is natural that solution H2S starts to dissociate into ion S2- and H+. Process dissociationoccurs because Am[concentration-of(H)] >ZERO and Am[concentration-of(S)] > ZERO, representinga process that will start or stop when the quantity of both ions pass through landmark values. Processdissociation of H2S is illustrated in Figure 11. As stated in quantity condition, the process will start orstop when there exists the substance H2S, and both the ion H+ and ion S2- are not saturated. As thedissociation rate is inversely proportional to the concentration of ion H+ and ion S2-, the dissociationprocess should continue towards completion as implied by the function +Qα . The dissociation process

will stop when there is no more H2S substance in the system. However, in an acidic environment, ionH+ initially exists in the system. The dissociation process that yields ion H+ and ion S2- quicklysaturates the system with ion H+ before the dissociation process completes. When another landmarkvalue [saturated] is met, some processes may stop or start. Correspondence is another kind ofinformation that can be specified concerning the function implied by Qα .

Correspondence((dissociation-rate(H2S), ZERO),(concentration(H),saturated(H)))

The correspondence statement means that if the concentration of ion H+ is greater than thesaturated, no dissociation should occur, and process dissociation should then cease. The system isnow saturated with ion H+, but due to incomplete dissociation, concentration of ion S2- would benegligible. Reaction will occur between ion Fe2+ and ion S2- to form black precipitation of FeS.Figure 11(b) illustrates the formation of precipitation process. In the formation of FeS, formation rateis proportional to the concentration of S2-, written as follows:

Correspondence((dissociation-rate(H2S), ZERO),(concentration(S),min-solubility-product(S)))

Fig. 10. Individual views describe objects and states of objects. Preconditions and quantity conditions arethe rules to obey, and relations stated the location of the objects.

Fig. 11. Process involves dissociation of H2S and precipitation of Fe and ";" denotes comment.

The solubility product of FeS is 4.0 X 10-19mol-1 under the room temperature. If theconcentration of FeS does not exceed the solubility product, then the substance will not beprecipitated. Therefore, 4.0 X 10-19mol-1 is defined as one of the limit points in the quantity space ofsolubility product of FeS, and is defined as ZERO in Figure 11(b) in the correspondence section. Asthe concentration of S2- is insufficient to meet the requirement of the solubility product of FeS due toan incomplete dissociation process, therefore, the black precipitation of FeS will not be formed in anacidic environment.

Equilibrium State

Equilibrium state is achieved for a particular reaction when the rate of forward and backwardreactions is equal. Equation 1 is a forward reaction when W and Z are mixed together to produce Xand Y. It is also possible for the system to reverse the reaction as shown in Equation 2. Equation 3shows that the chemical reactions on both side of the equation has reached equilibrium state wherethe rates of the forward and reverse reactions are equal.

In inorganic chemistry we can translate the above situation in a real chemical example as shownbelow.

H2S 2H+ + S 2- (black precipitation)

W + Z → X + Y (1)W + X ← X + Y (2)W + X X + Y (3)

If iron ions are mixed in the solution, a black precipitation will occur. It could be noted that theequilibrium system can be perturbated. For example in the case of H2 S, one of the ions (either H+ orS2-) can be saturated and the forward reaction will be hindered. In fact the reverse reaction will occurto reduce the H+ or S2-. This follows the Le Chatelier's rule and could happen when H2 S is mixed withHCl (acid) instead of water which increases the amount of H+, thus hindering the forward reaction.

This phenomenon can be explained as follows:The dissolution of H2S (reagent) in water produces H+ and S2- ions, until

equilibrium is reached between H2S and both ions. Since only S2- is used up toreact with Fe2+ to form the precipitation, H+ ion (which is an ion responsiblefor the acidic environment), remains unused. The accumulation of these H+

ions towards saturation in the system will hinder the dissolution of H2S. Thus,there are insufficient S2- ions to react with Fe2+, and the precipitate will not beformed. Adding HCl will thus hinder the forward reaction more extensively.

We attempt to generate this kind of explanation automatically in the system using QPT. In thispaper we focus on the functionalities of QALSIC as QPT implementation in QALSIC had alreadybeen described elsewhere (Pang, Syed Mustapha & Zain, 2001; Syed Mustapha, Pang & Zain 2002;Pang, 2003). We will show two situations where the system is able to generate explanations forunprecedented events. In the first case, we assume that the students are instructed to mix Fe with H2Sand use water as the media. However, if the students did not follow the instruction exactly and useacid as the media, QALSIC is still able to generate an explanation as discussed in the next subsection.In the second case, a student enters a chemical name that is not recognized by the system; will thesystem return some useful explanation or merely produce a blunt message such as "Unknownsubstance"?

QALSIC's Response for Unprecedented Cases

Figure 12 and Figure 13 show two generated explanations for different media environments (waterand acid respectively). Figure 12 describes that the black precipitation is FeS (FeS adalah mendakanberwarna hitam) and in Figure 13, the system generates "Dissociation of H2S is incomplete. Theprocess of FeS formation is incomplete. FeS may not be formed in this reaction." Figure 12 depictsthe result of the equilibrium state whereas Figure 13 describes the incomplete process of dissociation.The system does not have to be encoded with all possible events in its database before it can handlean unprecedented event. The system needs to have knowledge about what are the preconditions thatthe system must fulfill for the precipitation to occur. As shown in Figure 14, the process ofprecipitation will only occur if both Fe ions and H2S are not saturated and can be dissociated. Thesystem acknowledges that acid contributes H+ ions excessively during the process of dissociating H2S.As a result of that, H2S will not be able to dissociate any further as the amount of H+ and S2- areimbalanced. The broken link indicates that the process does not reach the precipitation stage.

The system is also able to generate such explanations even if different combinations of chemicalsubstances are used. In the next subsection, we would show that the system is able to predict thereaction even though the name of the substance is unknown to the system.

H2S

Fe

saturated (H)saturated (S)

Dissociation stops

dissociation_rate αQ- concentration (H)dissociation_rate αO- concentration (S)

Blackprecipitation

Dissociation Process==================H2S is a solution in Hydrogen Sulfide.Dissociation process begins... H2S appears as ion H and ion S insolution.H2S shows reversible reaction.

Fe is an ion Ferum(Iron) that dissolves in the water that formsion.

Process of forming FeS==================================Formation process begins... FeS appears as ion S and ion Fe insolution.

Writing Molecule Formula :=========================Oxidation number for S: -2 and Fe: 2Reaction occurs between cation Fe and anion S to form FeS.

Equivalent reaction:============================H2S + Fe = FeS + 2H

Observation:===========FeS is a black precipitation.

Fig. 12. Explanation generated under a neutral environment (translated into English).

Dissociation Process==================H2S is a solution Hydrogen Sulfide. Dissociation process begins ... H2S appears asion H and ion S in solution. H2S shows reversible reaction.Warning:Dissociation rate represents the dissociation process H2S. Dissociation process isindirectly proportionate to the H. Dissociation rate increases when the concentrationin H decreases. Dissociation rate stops when the concentration of H exceeds theconcentration level. Fe is an ion Ferum(Iron) that dissolves in the water to form ion.

Process of forming FeS==================================Formation process begins ... FeS appears as ion S and ion Fe in solution.Warning:Formation rate represents the process of forming FeS. Formation rate is directlyproportionate to the saturation of S. Formation rate increases when the saturation inS increases. Formation rate stops when saturation in S is less than the minimumsaturation level for the Ksp (K solubility product) for S in the system.

Writing Molecule Formula:=========================Oxidation number for S: -2 and Fe: 2Reaction occurs between cation Fe and anion S to form FeS.

Equivalent reaction is:============================H2S + Fe = FeS + 2H

Observation:===========FeS is a black precipitation.Special Note:==========Dissociation process H2S is incomplete. Formation process of FeS is not complete.FeS is not formed for this reaction.

Fig. 13. Explanation generated under acidic environment (translated in English).

Acid

Fig. 14. A simplistic view of the qualitative process for black precipitation (broken link indicates theprocess is incomplete).

Reasoning about an Unknown Substance

In inorganic chemistry, a substance is characterized by some of its natural properties such as theatomic mass and weight that are classified into a periodic table based on the atomic numbers. Thename for every element is associated with its designated symbol as well as its electronicconfiguration. This makes recognizing a substance a straightforward task for a computer. We equipQALSIC with basic chemical principles and facts such that the system is able to predict the behaviourby reading the chemical formulae (the symbols and stoichiometry). We tested a substance which isnot known to the system (i.e. the name is not stored in the QALSIC knowledge base). The unknownsubstance is mixed together with another known substance and the system is able to generate somepredictions as shown in Figure 15.

Acetic acid CH3COOH is an unknown to the system as stated on the first line of the right screen(CH3COOH adalah larutan tiada ketahui). The name of the chemical substance will be displayed ifthe substance is in the database. However, the lines following thereafter are explanation generatedabout the processes that took place when CH3COOH and Fe are mixed under acid media. Theprediction is a reasonable one which indicates that QALSIC is indeed able to generate responses forunprecedented events.

Experimental Simulation

In this section we shall demonstrate the application of the QALSIC system in the two environmentsthat we mentioned in the previous section (Extending the traditional chemistry laboratory). In thefirst environment, the student is working in an actual laboratory using QALSIC. This is equivalent tothe symbiotic environment in the wet laboratory described previously in Figure 1 where thequalitative analysis is done on the system (instead of workbook) and the experiment is done in the wetlaboratory itself. This environment is exhibited as in Case 1 where a student performs the experimentsbased on a certain sequence (as suggested by a book or teacher) and still manages to obtain similardeductions with the experiments in which the sequence of tests are arbitrarily ordered. In Case 2 astudent chooses an unknown from the system and performs the experiments independent from the wetlaboratory i.e. by using the qualitative simulation module as an alternative.

In Case 1, we assume that both experiments start with the same unknown sample from whichsimilar deductions should be obtained. In Example 1 below, the experiments are conducted accordingto the procedure suggested in the workbook while in Example 2; the sequence is reordered accordingto the student's choice. However, the reagents chosen are the same for both examples. Readers areencouraged to examine the difference in sequence of tests in Figure 16 and Figure 18 simultaneously.The reagents used in both situations are almost the same and the final deductions of ions ought to bethe same. The reports generated in Figure 17 and Figure 19 indicate that the conclusions (as circled)for both are accepted to be the same even though there are differences in additional ions that are beingdeduced.

The anion and cation expected to be present in the unknown substance are SO42- (sulphate) and

the Al2+ (aluminium) respectively. However, the conclusion in Figure 17 has Zn2+ (zinc) as anadditional deduced cation and in Figure 19, two extra anions are deduced; HPO4

2- and IO3

-. These ionsare deduced in the second test which says,

Fig. 15. Explaining the process for an unknown substance.

Example 1

System is able togeneratedescription aboutthe processes thattake place

System describesthe process ofdissociation isincomplete.

System commentedthat CH3COOH isan unknownsubstance

["SO4","C2O4","HPO4","IO3","S2O3"] hadir (present).

Fig. 16. Experiments according to the workbook.

Fig. 17. Report states the conclusion.

Example 2

Conclusion

Fig. 18. Experiment sequence according to the student's choice.

Fig. 19. Report on different order of experiments.

Conclusion

Sequence ofexperiments

Anions andcation detected

The system will not ignore the positive results which were obtained from any tests even though itappears only once. These additional ions can be further eliminated by running further tests using theSpecial Test (Ujian Khas) function.

The simulated example described in this subsection indicates that students are given leeway to beinquisitive by testing several possibilities that would bring about the desired result.

In Case 2, the experiment is performed entirely on the system. Initially, a student is given anunknown by the system. The unknown can be set by the teacher or randomly decided by the system.In our example, we presume the teacher has chosen a substance that has Pb2+ as the unknown ion forthe student to search for.

The student begins the experiment by testing with aqueous silver nitrate (AgNO3) and bariumnitrate (BaNO3) to narrow the possible set of ions. In Figure 20(a) the qualitative simulator predictsthat the white sedimentation of Pb(OH)2 + 2Na will form and the student can use this as anobservation to be reported in the qualitative analysis module. In Figure 20(b), the screen shows thepossible ions (Mg2+, Ca2+ and Ba2+) that may be present in the unknown as deduced by the systembased on the observation entered by the student. The system also suggests which reagent to use forthe next test to narrow down the possible ions. Figure 20(c) – (f) are screenshots of qualitativesimulation and qualitative analysis modules that indicate the alternate use of both systems in derivingions. Figure 20 (g) is the final report which concludes the existing anion and cation. The final reportindicates that the student managed to derive the probable ions as set by the teachers earlier. Thesecase studies exhibit the possibility of using QALSIC independently as an extension to, or independentof the traditional wet laboratory.

RELATED WORK AND FUTURE WORK

We consider two major works which are related to our project on the basis of deploying qualitativereasoning to develop educational software. CyclePad is an articulate virtual laboratory software forteaching engineering students to design thermodynamic cycles (Forbus, Whalley, Everett, Ureel,Brokowski, Baher, et al., 1999). GARP and VISIGARP are two related systems in which the latter isa visual tool to the former; to provide learning tools through model building. GARP and VISIGARPhave been applied to the domain of ecology (Salles & Bredeweg, 1997). CyclePad andGARP/VISIGARP are robust learning tools with different emphases. In this section we use these twosystems as comparative tools to evaluate QALSIC and to identify QALSIC's weaknesses andstrengths.

GARP is a generic tool for building knowledge process. It allows a student to build complexrelationships between objects in which the relationships are defined by qualitative process terms suchas quantity, quantity spaces, influence and qualitative proportionality (similarly implemented inQALSIC in the QPT database). The objects are created by defining the quantity, applying the quantityand assigning values to the quantity (Machado & Bredeweg, 2001; Bouwer, Machado & Bredeweg,2002). Subsequently, the so-called input-output dependencies are qualitatively simulated to generatepossible qualitative behaviours. Due to its complexity, VISIGARP was built to simplify itsrepresentation into different views. We believe that the idea of GARP is to provide a micro level viewof physical processes that would enable many possible qualitative simulations to be modelled.

(a) (b)

(c) (d)

(e) (f)

Mixing unknownand NaOH yields

white sedimentation(Pb(OH)2)+2Na

Possible ions that are presentare, Mg, Ca, Ba

System suggests using NH3(Ammonia aqueous) to testMg and Na2CO3 for Ca andBa

Mixing with NH3/NH4(OH) yieldswhite sedimentation

Pb(OH)2+2NH4System suggests that Pb, Al andMg may present

Mixing Kalium Iodida

Pb and Ag arededuced to exist inthe unknown

(g)

Fig. 20. (a), (c), (e) are generated from the qualitative simulation and (b), (e), (f) are generated from thequalitative analysis. (g) is the report produced from all of the tests which describe the observation and deduction

being made.

The relationships between states are more visible through the nodes and arcs inherent inillustrating the models. QALSIC, on the other hand, offers a different concept in that the qualitativerelationship is not visible to the students. Our reason is simply that the system addresses non-ITstudents at high school level. The explanation which describes the relationship of certain chemicalbehaviour uses natural language. GARP, nevertheless, is useful in building qualitative models asunderlying mechanism for simulating possible chemical behaviour. A thorough study is needed on theapplication of GARP to complex experimental scenarios for inorganic chemistry. This would involvedifferent types of apparatus within a single process, mixing more than one reagent at thesame/different times, considering ancillary factors such as pressure and temperature and also differenttesting modes (such as heating and burning). However, we feel that there is a need to build a specialinterface for GARP to accommodate users (students and instructors) who do not have sufficientunderstanding of qualitative process theory to be able to use GARP as a chemical QPT modeling toolin an effective manner.

CyclePad describes two types of thermodynamics process flow; closed cycle and steady-flowcycle (Forbus, Whalley, Everett, Ureel, Brokowski, Baher, et al, 1999). CylePad's approach isdifferent from GARP in that the knowledge is already built into it. Unlike GARP, the learning processrequires a student to design thermodynamic cycles and coach the student in resolving contradictionand making the student understand the teleology of the system. In our view, CyclePad has shown anin-depth and comprehensive approach in building articulate educational software which leads to awider room for improvement to QALSIC. QALSIC's approach is similar to CyclePad in that thedomain knowledge pre-exists in the system before the students begin to use it. However, CyclePadhas looked into a more detailed description of the domain theory such as the entities involved in thecycles, the possible modelling assumptions, the local and global equations that will be used toperform numerical calculations of the cycles, the property table of substances and the economicmodel which assists students in designing cost-effective systems. Based on this, the areas relevant forus to look upon in upgrading QALSIC are adding more entities in the qualitative simulation

Conclude that Pband Mg may exist

environment, extending modelling assumptions to adapt real world situations and defining a table ofreagents for different phases. Currently, QALSIC is only able to reason for reactions that occur in theliquid phase.

The design and analysis in CyclePad offers a wider selection of possible designs for the studentto choose whereas QALSIC has only one type of possible experimental mode in its qualitativesimulation. However, we have provided a sufficient number of observational categories in thequalitative analysis section for different kinds of reagent. The design and analysis in CyclePad isadaptable to QALSIC in its topological analysis, constraint propagation and sensitivity analysis.Currently, QALSIC does not allow students to design complex experiments; nevertheless, topologicalanalysis is necessary when more components are involved in a single test. Topological analysis ininorganic chemistry can prevent an error-prone and hazardous experimental set-up. The idea of aconstraint propagator in CyclePad may be useful in configuring molecular formula as there are rulesto obey. However, the application may differ in the context of a chemical equation. Sensitivityanalysis is also applicable in inorganic chemistry as much as in thermodynamics because chemicalreactions behave differently under parametric values such as temperature, pressure and amount ofsubstance but these are not regarded seriously in the current version of QALSIC.

Another aspect of future work is in developing a graphical interface which is able to demonstrateanimated chemical reactions besides the natural language explanation. A special graphical language isalso probably needed as the interface between the graphic generator and the reasoning engine.

QALSIC is a proof-in-principle system that has not been fully tested in the actual schoolenvironment. The system as it is now requires special training to the students and teachers beforeusing it especially in preparing the simulation database. The system requires further refinement on theinterfaces such that the users do not need to learn the concept of qualitative processes to set up such adatabase. Another major work that is required is to provide graphical animation on the chemicalreactions besides the chemistry explanation generated automatically. QALSIC will then be ready toexplore real environments when these features are embedded.

CONCLUSION

QALSIC is an educational software which addresses the flexibility in learning inorganic chemistryand the deployment of artificial intelligence techniques to generate explanation on aberrant chemicalsituations. In this paper we focus on the functions of the system in delivering the criteria of anarticulate software as suggested by Forbus (2001). We discussed the first criterion (fluency) whichrequires a system to be able to understand and communicate the content of the knowledge that it has.The factual knowledge of chemistry is represented in the form of rules and facts. The factualknowledge prevents any possible mistakes made by the students concerning commonsenseknowledge. For example if one reports "white sedimentation" upon testing a substance which wasreported as "black sedimentation" in the earlier test, the system will respond in a preventive manner.This type of knowledge also prompts the student in advance of a potential event after analyzingreports of past observation. For example, the system would prompt the student to report a bluesedimentation upon dissolving a particular substance in excessive ammonia. The responsivebehaviour between the knowledge of the system and the action of the students is an indication that thesystem understands the student's action and expresses the contents of its knowledge accordingly. Thesecond criterion (supportiveness) states the need for coaching and tutoring in problem solving. We

address this issue by allowing the student to demand assistance only when needed from the system.Students can choose to determine his/her deductions from the observations or refer to the system'sdeductions. The system also suggests potential tests and reagents to be used in order to reduce thenumber of candidate ions present in an unknown. The students may or may not follow thesesuggestions. The system encourages intuitive learning. The third criterion (generativeness) is theability of the system to handle new problems for which solutions are not preset. We illustrate thiscapability by testing the response of the system on an unknown substance and on reactions carried outin an unknown media. Unknown substance refers to a substance with a descriptive name that is notretrievable from the database. As a response, the system uses the IUPAC convention to derive theproperties of the substance. Unknown media refers to a media which is not commonly used to mixtwo ions. In both situations, the system is able to generate an explanation using fundamentalprinciples of chemistry. The fourth criterion (customizable) allows instructors to modify, update andextend the libraries of phenomena, designs and domain theories used by the software without the needto mend at the programming level. QALSIC provides a QPT database for the instructor to definequalitative relationships such as the correspondence and qualitative proportionality between processesand objects. The factual knowledge such as the color of sedimentation, the properties of gas, the testsfor gas, color of the dissolved substances, the types of irreversible reactions and chemical namescould also be added and modified without the need to meddle at the programming level. Theemphasis in our approach is on the flexibility of generating an explanation which is derived from thebasic principles. Explanation which is generated from the rigid rule-based is similar to inculcating'spoon feeding' to the student.

ACKNOWLEDGEMENTS

We would like to thank the University of Malaya for giving financial support under thePascasiswazah grant for a two year period and Vote F for a year. Similarly, we would also like tothank all the reviewers for their constructive comments on this paper.

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