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Computer Based Learning Environments in Teacher Education: Helping Students to think Accurately, Boldly and Critically

Lynette Schaverien and Mark Cosgrove

l.schaverien@uts.edu.au, mark.cosgrove@uts.edu.au

University of Technology, Sydney

 

Abstract

Interactive multimedia-based learning environments have been designed to help tertiary students achieve deep understanding as they learn to teach science. These environments enable individualised, flexible learning and have proved to be successful, one in its prototype stage and the other as a completed version.

Introduction

The theme of this conference, What works and why? permits us to

We locate our stories at a cross-road where the educational practices of one era are intersecting with those being formulated for another. As Tiffin and Rajasingham (1995) point out (Table 1),

It is our view that present technologies can deal effectively with the provision of education anytime and anywhere. Here, we aim to provoke discussion of information era teaching methods and learners' roles by suggesting that the two learning environments we will demonstrate, underpinned as they are by a particular learning theory, might well constitute prototypical examples of what Tiffin and Rajasingham (1995) envisage.

We have developed a theory of learning (Schaverien and Cosgrove, 1997) which posits that the heuristic, generate-test-regenerate (Minsky, 1985; Plotkin, 1994), is natural, and, as such, available to all learners since it is built into the brains and genes of all living things. This theory grew out of two major empirical studies, reports of which have now been published in international journals (Cosgrove, 1995; Schaverien and Cosgrove, 1997). The findings of these two projects suggest

  1. that students' learning of seminal science ideas is well-explained as a process of generating and testing (hence selecting) of ideas on their value, that is by means of a Darwinian generate-test-regenerate heuristic, and
  2. that teachers' learning to teach (in ways that support such natural learning) can be understood in terms of that selectionist heuristic as well.

 

Table 1. Education in pre-industrial, industrial and information technology societies (After Tiffin and Rajasingham (1995))

 

Pre-industrial

Industrial

Information era

Learners

Young of the elite

All young people

Everyone

Language

Latin and Greek

National language

English

Age of learners

6- 20 years

6 - 16 years

Any

Payment

Parents

Taxes

Users

Provider

Church

State

Corporations

Economic system

Traditionalist/Medieval

Taylorist/Fordist

Neo-liberal/Post-Fordist

Where available

Sites of knowledge

Town schools

Anywhere

When available

Arranged times

Set times

Anytime

Source of curric.

Teacher

State

Learners' needs

Teaching methods

Master-Apprentice

Transmissive

?

Learners' role

Imitative

Passive/receptive

?

However, these reports demonstrate that although the radical re-learning required in both cases is possible, it occurs at a price: those interventions by a teacher-cum-mentor which brought this re-learning about were intensive, making them expensive and difficult to replicate. Many teachers lacked insight into them and lecturers considered them undignified in tertiary lectures.

So, a way of scaling up these interventions was required, as was a way for students to gain access to these ideas. We turned to other forms of mediation than human teacher-mentors, forms which might constitute information age learners' roles and teaching methods. Following Laurillard (1993) we reviewed a range of media, from books to interactive multimedia (IMM), arriving at the view that an IMM-supported base offered the opportunity we sought.

A Generative Virtual Classroom

In demonstration 1 the learning environment is a Generative Virtual Classroom. This World-Wide-Web-based learning environment permits students to observe and discuss authentic examples of children learning. Here, students can deepen their insights, over time, into teaching events by:

  1. viewing digitised video excerpts of children learning, with their transcripts,
  2. recording, in a database, features of children's learning, including the key ideas children generate, how they test those ideas, the progression of those ideas during the chosen video excerpt, characteristics of children's conjectures and other insights they have into those learning events (such features being critical to an appreciation of science learning as knowledge generation and refinement),
  3. searching the expanding database archive for their own views and those of other students,
  4. listening to pre-recorded audio commentaries which cast each learning event as a significant example of knowledge generation,
  5. conversing with each other and with their tertiary teacher (by means of a supporting threaded email discussion group) with respect to the learning they observe, and
  6. reflecting upon their own learning and teaching as a conceptualising process, that is, as knowledge generation.

The project has links with three established World Wide Web based learning environments:

  1. The Virtual Hospital at the University of Iowa Medical School.
  2. The Australia Street Archive, in which users write to the archive, adding to its richness and versatility.
  3. Learning Constellations which employs video-ethnographic methods, emanating from the Media Laboratory at MIT to capture authentic science learning and teaching in local classrooms

As well, the project has a well-recognised research base. That research suggested that in order to adopt innovative science teaching approaches, teachers needed to be helped to relinquish their view of learning as occurring only by being instructed. Only then could they perceive learning as generative (after Wittrock, 1974; Minsky, 1985; Edelman, 1992 and Plotkin, 1994), as occurring when learners generated ideas and tested them on their value. The support of a mentor appeared to be critical in assisting teachers to see more fruitful ways of teaching. Furthermore, that mentor support, with its sustained and frequent conversations between teachers and the mentor, appeared to contribute, by its very nature, to teachers' developing understanding of generative styles of teaching. So, this project preserves those aspects critical to successful mentoring, designing them into an electronically mediated learning environment. The GVC encourages students to attend to the development of children's ideas through electronically mediated conversations.

Computer-based Laboratory

In demonstration 2 the learning environment is a free-standing (= anywhere, anytime) laboratory in which individual students can identify and examine their ideas about a conservation principle. This environment, incorporating screen-based images (including animation) and simple laboratory equipment, assists students, first, to diagnose the status of their own knowledge and to compare that with scientists', and secondly, to generate and test a new theory from which accurate predictions of circuit phenomena can be made.

Do they work?

Evidence of the success of these two learning environments is accumulating.

Generative Virtual Classroom

The effectiveness of the first of these learning environments is being investigated by case study. At the time of writing, the first phase of development of the Generative Virtual Classroom has been completed. It exists as a fragile, working prototype. As part of this first phase of development, we have conducted two case studies so far.

In the second of those case-studies two students worked with the Generative Virtual Classroom, unassisted, in sessions ranging from one to three hours in length, once or twice a week, over a period of three weeks, spending approximately six hours altogether. We did not observe these sessions. At the end of the sessions, we audio-taped a conversation with one of the students (Susan). On analysis, this conversation reveals evidence of learning reminiscent of teachers in the mentor-supported research projects on which the design of the Generative Virtual Classroom was based. For example, in that previous research, one teacher (Jan) came to realise that her daughter Sarah's actions in connecting up a circuit, far from being "primitive" as she had earlier described them, were actually tests of Sarah's particular theory. Similarly, Susan not only began to deepen her criteria for appreciating the worth of what children did in the Generative Virtual Classroom, but she was aware that she was doing so.

Jan's and Susan's appreciation that these children were actually generating and testing their ideas, in circumstances where these teachers had earlier failed to recognise that, is a critical indication that they are beginning to develop a generative view of learning. That Susan can come to this same significant realisation as Jan, but in a computer-mediated learning environment, is a promising indication that the design of this learning environment has been successful in preserving those features of the mentor-supported learning environment that are critical for developing a generative view of learning.

Computer-mediated Laboratory

The principal users are graduate students in Science and Technology Education subjects in the Graduate Diploma of Education. The laboratory was presented in a flexible learning module.

Sample of Students

Volunteers in a study of learning through interactive multimedia. N = 20, Names = 01, 02, 03 .... 20. Mean age = 26 years, mode 22 1 years, with 10 females. The survey enquired into their physics education; first, participants were asked to indicate the most advanced level at which they had studied physics (varying from year 10 to year 15). Next, the participants were asked if they enjoyed learning Physics. Of those (10) who did, eight were male. The security of their knowledge about electricity was established in a question, 'Do you have a deep understanding of the topic, 'Electricity'?

Claiming to have a deep understanding of electricity

In this sample, 13 students thought they did not have a deep understanding of this topic.

Yes 03, 10, 11, 17, 18. I did electronics in Year 2 (03); Relative to school level, yes (10)

Fair 07, 20. I have a general understanding of it (20)

No 01, 02, 04, 05, 06, 08, 09, 12, 13, 14, 15, 16, 19

Next, students completed six questions about simple circuit phenomena, such as drawing a circuit for a torch, placing lights in series and parallel, commenting on relative brightness levels of lights and expiry rates for cells in different circuits. Typical responses for one question is listed in Table 2.

Such a proliferation of responses is an effective probe of students' confused knowledge states and some would say, a profound worry on two scores; first, that even after extensive teaching these students cannot use the scientific way of thinking about even very simple phenomena, and second, that these people were expected to teach secondary school students about this phenomenon.

Table 2. Comparing the brightness of lamps in three circuits, where Lamp A is in a circuit by itself, Lamps B and C are in series, and Lamps D and E are in parallel.

The set of 20 students produced ten different answers, including 'I don't know'. The nine groups of answers which attempted the comparison are listed in five sets.

Set 1. In which there is one level of brightness amongst the five lamps.

A=B=C=D=E One answer [20]

Set 2. In which there are two levels of brightness.

A=B=D=E>C One answer [07]

A>B=C=D=E Two answers [03, 18]

A=D=E>B=C No answers. This, however, is the correct answer.

Set 3. In which there are three levels of brightness.

A>D=E>B=C Six answers [01, 11, 13, 14, 15, 16]

A>B=C>D=E Two answers [12, 17]

Set 4. In which there are four levels of brightness.

A>C>D=E>B One answer [10]

Set 5. In which there are five levels of brightness.

A>B>C>D>E One answer [02]

A>D>B>C>E One answer [08]

A>D>E>B>C One answer [09]

No answer 04, 05, 06, 19

 

Once this survey had been analysed the results were returned to the students in the form of a report which presented

  1. a summary of research literature on the issue of deep versus shallow learning,
  2. the results for all members of the class, reported anonymously, and

suggestions for further work, including individual diagnosis using the information in Table 3 with which students diagnosed their own learning state and designed the treatment needed.

Domain knowledge

Students' knowledge

Electricity is differentiated into charge which flows, a force which pushes it and resistance that hinders that flow.

Electricity is an undifferentiated entity.

Charge flows.

Current flows.

Charges (electrons) exist in the wires as well as in the conductors in the cell.

Charges (electrons) which flow in a circuit come from the cell.

A resistor hinders the flow of charges.

A resistor consumes the current.

Two resistors in series hinder the flow more than a single resistor does.

Two resistors in series consume more current than a single resistor.

Two resistors in parallel provide less resistance to the flow of charge so the charge flow increases.

Resistors in parallel successively divide the current.

A cell is not a source of constant current.

A cell is a source of constant current.

A circuit is a system.

A circuit is a linear arrangement of parts.

Resistors may be ohmic or non-ohmic.

All resistors follow Ohm's rule.

Table 3: Contrasting domain knowledge with that of students

With this information students were asked to comment upon the results of the first survey and then to devise a plan to achieve deeper understanding. An extract of one student's comments in presented in Table 4.

Table 4. Extracts from one student's response to survey 1.

Flexible Learning Diagnostic Report student #CR

I found the electricity survey a particularly difficult task. This was partly due to the fact that I never had a deep understanding of the topic "electricity" and that I found (that) all I needed to succeed in exams were a few equations. When I received the analysis of the survey, it was extremely clear that my ideas, and many of my fellow students, were a mixture of real scientific concepts and those that we had personally formed. It appears I have developed my own model for the electricity concept. My responses to the survey questions are evidence of this.

Item 3.11

In a circuit including three identical lamps A, B and C, and B in series with C. Predict what would happen to the brightness of the lamps A and B after lamp C was removed from the socket. It was a surprise to me that A remained unchanged yet B and C went out. It seems that a number of students believed as I did that lamp A increased, and lamp B goes out (implying that currents compensate). I'm sure this is the result of believing that a circuit is a linear arrangement of parts (student knowledge). Is this a case of science students trying to find a mathematical solution?

From this survey it is obvious that past poor pedagogical practices have resulted in my passing high school, and first year university physics examines with very little understanding (if any) of electricity. My study of many physics topics has been one of manipulating mathematical equations. Through this exercise, I find that I have no real understanding on how electricity and resistors work.

 

Some weeks later, the students were presented with a second survey with questions similar to those in the first survey, but with the further requirement that reasons or explanations be provided for each answer. The full data for the second survey is summarised in Table 5. Here, the first listed y indicates a correct answer, and a second listed y indicates a correct reason. X indicates wrong answer or wrong reason.

Compare the outcomes of survey 2 question 3 with the information in Table 2. In the second survey 18 students wrote the correct answer and 13 provided a correct explanation of the answer.

 

Table 5. Summary of responses to Survey 2

Name 2S.3 2S.4 2S.5 2S.6 2S.7 2S.8 2S.9 2S.10 2S.11 2S.12 2S.13

01 yy yy yy yy y yy yyy yyy y xx xxxx

02 yy yy yy yy y yy yyy yyy y yy yyyy

03 y? y? yy yy y y? yy? yyy y yy yyyy

04 yy yy yy yy y yy yyy yyy y xx yyyy

05 yy yy yy yy y yy yyy yyy y xx yyxy

06* yy xx yy xx y xx xxx yxx y xx xxxy

07

08 y? yy yy yy y yy yyy yyy y xx yyyy

09 yy yy yy yy y yy yyy yyy y xx yyxy

10

11 yy yo yy yy x yy yyx yyy y yy yyyx

12 yy yy yy yy y yy yyy yyy y xx yyyy

13 yy yy yy yy y yy yy? yyy y yy xyxx

14 yy yy yy yy y yy yyy yyy y xx yxxx

15 yo yy yy yy y yo yyo yyx y xx yyxx

16 yy yy yy yy y yy yyy yyy y xx yyyy

17 yy yy yy yy y yy yyy yyy y yy yyyy

18 xx xx yy y? x yy yxx yxx x yy yyyy

19 yy yy yy yy y yy yyy yyy y xx yyyy

20

ra 17 15 17 17 17 17 16 18,16,14 6,6

rr 13 13 17 14 15 2

Why do they work?

Research in multimedia and learning has been criticised by Clark and Craig (1992) on the basis that, "aside from obvious research design problems, the main obstacle to multi-media and learning studies is that they are conducted without any benefit of why one would expect differences in the first place". By contrast, the design of these two learning environments explicitly incorporates findings of previous research, leading us to expect that they will be effective in promoting student learning. Features designed into these learning systems which may be significant to the re-learning that both groups of students demonstrated include

Both learning environments described here have been designed around a well-researched view of learning science. This view asserts that learners generate understandings by testing ideas on their value). Thus, students in these two learning environments are assisted to generate and test the empirical worth of their ideas (about learning science and about a conservation principle respectively).

We began this paper by noting that we have reached a cross-road between the educational practices of industrial and information technology societies and by proposing that it is critically important, at this juncture, to consider not only the features of I.T. education but also how decisions will be made about the effectiveness of the learning and teaching aspects of the I.T. era.

References

Clark, R. and Craig, T. (1992). Research and theory on multi-media learning effects. In Giardina, M. Interactive multimedia learning environments; human factors and technical considerations on design issues. Berlin: Springer-Verlag, pp. 19-30.

Cosgrove, M. (1995). A study of science-in-the-making as students generate an analogy for electricity. International Journal of Science Education, 17(3):295-310.

Cosgrove, M. and Schaverien, L. (1996). Children's conversations and learning science and technology. International Journal of Science Education, 18 (1): 105-116.

Edelman, G. (1992). Bright air, brilliant fire: On the matter of the mind. London; Basic Books.

Goldman-Segall, R.(1990). Learning constellations: A multimedia ethnographic research environment using video technology to explore children's thinking. Unpublished doctoral dissertation, Massachusetts Institute of Technology, Camb., MA.

Harel, I. (1991). Children designers: Interdisciplinary constructions for learning and knowing mathematics in a computer-rich school. Norwood, NJ; Ablex.

Laurillard, D. (1993). Re-thinking university teaching. Milton Keynes; Open University Press.

Minsky, M. (1985). The society of mind. New York: Heinemann.

Plotkin, H. (1994). The nature of knowledge. London; Allan Lane, the Penguin Press.

Schaverien, L. and Cosgrove, M. (1997). A biological basis for generative learning in science. Perspectives in Human Biology, 3 (pages not known).

Tiffin, J. and Rajasingham, L. (1995). In search of the virtual class. Wellington; Routledge.

Wittrock, M. (1974). Learning as a generative process. Educational Psychologist, 11(2):87-95.

 

(c) Lynette Schaverien and Mark Cosgrove

 

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