Martin Fischer, Ludwig-Maximilians-University Munich,
Germany
CASUS: An authoring and learning tool
that support diagnostic reasoning
Summary
Zusammenfassung
folgt
folgt
1
Introduction – Why CASUS?
Over the last years a number of multimedia authoring systems evolved for off-line
and, more recently, also for online presentation of interactive educational software.
Nevertheless, all of the available software packages require technical skills including
scripting for advanced features on top of a pedagogical concept, graphical artwork and
multimedia content data. These requirements limit the number of content experts as
potential authors not only in medicine. In consequence, multimedia productions
require interdisciplinary costly teams including software developers, instructional
psychologists and graphical artists in addition to the content experts and the audiovisual content data.
On the one hand this dilemma led to a limited number of high quality multimedia
titles in medicine covering all mentioned areas of expertise – “hand-crafted” pieces of
artwork.
On the other hand several customized medical authoring environments were
developed to allow a more efficient compilation of well-structured medical content.
This approach resembles an assembly line and restricts the freedom of the authors
significantly. Despite of all the efforts to make authoring as easy as possible, most
systems were unable to generate a critical mass of content.
It was shown that medical students in their clinical education in Germany
predominantly collect data and are mostly unable to apply their knowledge to a
hypothetical deductive clinical reasoning process (Gräsel & Mandl 1993). We
concluded that these difficulties were at least in part due to too little exposure to
relevant clinical problems. There was no clearly defined list of key clinical problems
to be covered by each student available at the university of Munich.
1
ZSfHD 3/99
In light of the portrayed educational problems and the ambivalent technical
situation we decided to create another authoring system - CASUS - that allows clinical
content experts to quickly create well structured case studies without programming
skills. These case studies were intended to round up bedside teaching by enhancing
the clinical reasoning processes for all relevant clinical symptoms and syndromes.
Peer reviewed case collections were envisoned for all medical students nationwide
covering the whole clinical curriculum. This was in 1994.
This paper describes the concept of the CASUS-authoring system with a special
focus on the support of the clinical reasoning process and evaluation data on authoring
and learning with CASUS.
2
The concept of CASUS
Constructivist approaches to learning and teaching emphasize two characteristics
of learning environments which should impart useable knowledge (e.g. Cognition and
Technology Group at Vanderbilt 1992; Collins et al., 1989): (1) Authenticity: The
learning problems used should be similar to real problems in their central
characteristics. (2) Complexity: The complexity of the problems should not be
reduced too much, and above all should be presented embedded in an authentic
context. In order to achieve an appropriate level of authenticity, it is requested of the
CASUS authors that they include diverse multimedial elements (e.g. audio, pictures,
and video) while designing their cases. Also, the authors are encouraged to use as
many interactive elements as possible during the design of the learning case; templates
for these are provided by the authoring system.
It has been emphasized repeatedly that learners should be appropriately supported
while working on a case (Cognition and Technology Group 1992; Gräsel 1997;
Leutner 1992; Stark, Gruber et al. 1998). Therefore, CASUS encourages the authors to
make use of the following support elements:
(1) Expert commentary. The authors are asked to generate their comments on all
important phases of a case to allude the learner to important or conflicting evidence
and give reassuring feedback from a personal point of view.
(2) Mapping-Tool (Network-Tool). Every educational unit within a case is related
to a graphical representation of the case: I allows the author to generate differential
diagnostic hypothesis and connect them with the respective clinical findings.
Finally the authors are stimulated through tools within the software to develop a
linear sequence of the flow of clinical information for the case before the media
assembling process begins: they are assisted in choosing an appropriate (with a
corresponding complexity) learning case. An easy to use computer tool, the
Befundmatrix (findings matrix), is made available for the collection and sequenzing of
the case story (Figure 2).
ZSfHD 3/99
2
With this tool the relevant clinical findings of a case are easily ordered by drag &
drop interaction. The next cucial step in the creation cycle of a learning case relates to
the differential-diagnostic network. Through this step the case author reconstructs the
entire diagnostic reasoning process by connecting the clinical findings with related
differential diagnostic hypothesis.
CAS US le arning syste n
CAS US
One emphasis of research on medical education is, how university education in the
subject Medicine can be improved through increased implementation of problembased learning. The expectation from problem-based learning is, on the one hand, that
knowledge acquired in a course of studies can be better used in clinical practice, and,
on the other hand, that the capability and motivation for lifelong learning is promoted.
Many theoretical and empirical studies concentrate on the development and evaluation
of problem-based curricula for universities (e.g. ACME-TRI Report, 1993; Albanese
& Mitchell, 1993; Das Arztbild der Zukunft, 1993). The fundamental idea of problembased curricula centers around using clinical cases, which serve as the starting point
for a study session of several hours in a small group led by a tutor. Using these cases
as examples the students analyze pathophysiological processes which characterize
them, identify the course's educational objective, work on the appropriate material,
and finally check to see if they have achieved their educational objective. This kind of
problem-based learning originated from Barrows (e.g. Barrows, 1985; Barrows &
Tamblyn, 1980) and was further developed and evaluated at various universities (e.g.
Armstrong, 1997; Gijslaers & Schmidt, 1990; Moore, Block, Style, & Mitchell, 1994;
Tosteson, Adelstein & Carver, 1994; Williams, 1992).
Another direction of research, which was pursued in the project being portrayed,
concerned itself with the design of problem-based media that are both, implemented in
university courses and offered in self-directed learning courses. In particular
computer-aided learning programs are suited as a medium for learning based on cases:
The integration of multimedial elements makes a realistic and authentic presentation
of clinical cases possible; through the use of interactive elements the students are
encouraged to actively tackle the problems. There are hardly any sound theoretical
approaches for the design of problem-based learning programs — many of the
existing programs were produced with intuitive didactic. Furthermore, the
development of learning cases with conventional authoring-systems requires
information technology knowledge as well as a large investment of time — both have
prevented instructors at universities and academic teaching hospitals from producing
computer learning cases to any noticeable extent.
The goal of this project was to remedy this deficit: A learning system (CASUS) for
medical education was developed comprising two parts (Figure 1): (1) CASUSLearning cases are multimedial learning programs for various clinical pictures which
the students work through from the perspective of a physician. (2) The CASUSAuthoring system should enable physicians to produce learning cases with minimal
technical effort, and should support them in the didactical design of the case.
______________________________
authoring
system for the
creation of
cases
authors create
CASUS-cases
case library
for learners
lerners aquire knowledge and diagnostic
and therapeutic strategies
Figure 1
______________________________
A didactical concept that is oriented toward constructivist approaches to learning was
developed for CASUS (M. R. G. Fischer et al., 1996). These approaches have
produced theoretical foundations, concrete instruction models, and empirical findings
on problem-based learning environments (e.g. Cognition and Technology Group at
Vanderbilt, 1992, 1993; Collins, Brown & Newman, 1989; Duffy & Jonassen, 1992).
In the following paper we will first introduce the problem-based concept of the
authoring system CASUS. We will then present results of a formative evaluation study
of the authoring system.
In many development projects of learning software the formative evaluation has been
given insufficient importance. The learning programs are mostly produced under a
deadline and difficulties and program flaws do not become apparent until the finished
programs are used — these are then seldom fixed after the fact. In contrast, during the
development of CASUS, a particular emphasis was placed on formative evaluation
which ensured improvements in the program while it was being developed. The
questions to be posed in the formative evaluation study were determined by the
objectives of CASUS: (1) Can clinically active authors produce learning cases on their
own without technical difficulties? (2) Could the didactical concept of problem-based
learning be put into action by authors designing their own cases?
CASUS is generally oriented toward the order of events in a genuine examination. The
steps in the examination make up the 'chapters' or didactic units of a learning case
which a student works through one by one. During the production of a learning case
this scheme can be varied by adding further chapters or through 'jumps' or 'loops'. In
order to achieve the highest level of authenticity it is further requested of the authors
that they include diverse multimedial elements (e.g. audio, pictures, and video) while
designing their cases.
A further principle is that knowledge is always actively constructed by the students in
a given context. From a constructivist perspective the learning environments should
stimulate the students to actively elaborate and interpret the information in the
learning material, and furthermore to use their prior knowledge on the problem, find
their own way to a solution, and independently fall back on additional information
sources. In this way, the teachers are not serving up a prepackaged 'knowledge mix',
rather they are supporting and correcting the independent learning process.
Correspondingly, the authors are encouraged by CASUS to use as many interactive
elements as possible during the design of the learning case; these are provided by the
authoring system.
The Authoring System CASUS
In the following section we will present the central principles of problem-based
learning based on constructivist approaches, and scetch their realization in CASUS.
Constructivist approaches to learning and teaching emphasize two characteristics of
learning environments which should impart useable knowledge (e.g. Cognition and
Technology Group at Vanderbilt, 1992; Collins et al., 1989): (1) Authenticity: The
learning problems used should be similar to real problems in their central
characteristics. (2) Complexity: The complexity of the problems should not be
reduced too much, and above all should be presented embedded in an authentic
context. In order to take these criteria into account, the structure of a learning case in
1
ZSfHD 3/99
Authentic and complex problems should stimulate the students to active and
constructive learning processes. However, studies in Medicine show that students are
often overwhelmed by the complexity of the problems and are not stimulated to
appropriate learning processes (Gräsel & Mandl, 1993; Gräsel 1997). Similar results
have also been found in other domains (Cognition and Technology Group, 1992;
Leutner, 1992; Stark, Gruber, Renkl & Mandl, 1998). A conclusion of these
investigations is that students should be appropriately supported while working on a
case. Therefore, CASUS encourages the authors to take the following support
elements into account: (1) Expert commentary. The authors are asked to prepare
comments, which contain an expert's views on the case. These comments should
demonstrate the process of diagnosing to the students during the entire time they are
working on the case, and thus stimulate them to form their own hypotheses. In
constructivist approaches the context-related articulations of an expert play a central
role (e.g. Collins et al., 1989). For learning with computer-aided cases in Medicine,
empirical investigations showed that comprehensive explanations by experts
contribute to the amount students learn while working on a case (Gräsel & Mandl,
1993; Gräsel, 1997).
(2) Mapping-Tool (Network-Tool). Every didactic section ends for the students with
the request to graphically represent the findings of the case, their interrelations and
their connections to hypotheses (Figure 2).
Findings show that graphical representation of a case with a mapping process is an
appropriate aid for learning with computer-presented cases in Medicine (F. Fischer,
1998; F. Fischer, Gräsel, Kittel & Mandl, 1996). For one thing, the use of a mapping
process supports the application of suitable strategies of diagnosis in working on a
case. For another, the students themselves can evaluate at a glance the completeness
ZSfHD 3/99
2
and coherence of their own problem solution, and correct themselves accordingly. In
an empirical study it was shown that a student's reflection on his/her solution is
particularly effective for learning when the student is encouraged to compare his/her
solution with that of an expert (F. Fischer et al., 1996). This finding was taken into
consideration in CASUS: The students have the opportunity to compare their networks
with an expert solution after every didactical unit.
Instructional supports for the Author. The program CASUS is supposed to offer a
sound instructional support for the design of cases. To achieve this the authors are
given a handbook which contains the basic ideas of the problem-based approach as
they have been summarized above. The principles of problem-based learning are
furthermore demonstrated through an example case which the authors can view.
Finally the authors are stimulated through various tools to develop a comprehensive
and well thought out concept for a learning case before the concrete programming of
the case begins. For example, they are assisted in choosing an appropriate (with a
corresponding complexity) learning case. An easy to use computer tool, the
Befundmatrix (findings matrix), is made available to the authors for the collection and
ordering of patient data (Figure 3).
With this tool the central findings of a case are ordered with drag & drop interaction
helping to plan the rough outline of the entire learning case. Furthermore, in this
graphic organization of the learning case it is already possible to mark positions where
multimedial material will be necessary. The next step in the programming of a
learning case consists of producing the differential-diagnostic network for every single
program step. Through this step the author must reconstruct the entire process of his
reaching a diagnosis; this results not only in a central support for the students, but also
in a general idea of the goals and content of his learning case. Only after this
groundwork does the actual design of the learning case begin, namely the work with
the individual screens. The authors are asked to design the screens one by one as
chapters. In order to arrange the information for each page there are seven different
types of forms on 'index' cards (Figure 4) available to the authors (for example: card
type 1 combines information, multimedia, and question-answer frames).
This paper will report on the primary evaluation study which was carried out with the
first beta-version of CASUS. Individual components of the authoring system (i.e. the
handbook, the Befundmatrix (findings matrix)) had already been evaluated before this
study. Experts in Medicine and Instructional Research evaluated the components in
the sense of a quality analysis. Furthermore, potential authors assessed these
components in respect to their practicability and understandability while the program
was still in the early stages of development. The study which is reported here was
aimed mainly at improving the authoring program CASUS. For this purpose, detailed
information about difficulties in producing a case was necessary; thus, a qualitative
research method was used: The process of producing a case was observed and
analyzed in order to derive consequences for further development from the results.
2.1
Research Question of the study
The study pursued mainly the question of which difficulties arise while producing a
case with CASUS and which measures are necessary in order to remedy these. This
was done by investigating both the technical aspects (use of the technical functions of
the program) as well as the didactical aspects (application of the concept of problembased learning) of producing a case. Additionally, the acceptance and motivation of
the authors in the use of the authoring system were elements to be investigated.
2.2
Method
After the completion of the first beta-version of CASUS four persons were asked to
produce a learning case with the authoring system in their respective specialty areas.
The test subjects came from the target group of the authoring system, namely they are
all experienced clinicians who teach at the university level and who are open to the
idea of problem-based learning. The authors had neither any special prior technical
experience (i.e. with other authoring systems) nor special prior knowledge about the
concept of problem-based learning.
Before the subjects began with the programming of a case they were told (1) to work
through the introductory chapter of the handbook, (2) to take a look at a demonstration
case, (3) to choose a case which they considered suitable for their students and (4) to
determine and produce the necessary multimedia material. The conditions under
which a case was produced were to be as similar as possible to those which later
authors would experience. Therefore, the authors worked mostly independently with
CASUS. The following data were recorded:
Acceptance of the authoring system and motivation in case producing. In a structured
interview after the production of a case, and with the help of a questionnaire, the
acceptance of the authoring system and the motivation while producing a case were
recorded.
1
ZSfHD 3/99
Difficulties and interventions in case producing. The process of case producing was
observed by a member of the CASUS development-team who wrote a detailed
protocol. Whenever difficulties with case producing arose the observer intervened in
the process. Based on a question checklist he asked the author about the causes of the
problems and possiblities for remedying them. Thus, the observer and the author
worked out a solution to the problem area together. The computer logfile protocols as
well as a subsequent interview were further sources of information about problems
and their solution in the interaction with CASUS. Both technical and didactical
difficulties were recorded by the observer.
The observers' protocols and the subsequent interviews were evaluated by structured
extraction of key information. The difficulties were classified as being either technical
or didactical. In the following, only difficulties of interaction with the program are
reported; software problems (bugs) are not taken into consideration. Of course, these
were recorded and fixed in further program development, too.
2.3
Results
Acceptance and Motivation. The authoring system was met with a high level of
acceptance: For one, the multiple-choice questions yielded — almost without
exception — high acceptance values; for another, positive assessments of the learning
system predominated in the interviews (e.g. "the authoring system is altogether well
suited for producing learning cases" or "I would also recommend the authoring system
to others"). The critical comments referred mostly to software problems (e.g.
computer crashes) which were still present in the first beta- version. The picture
painted in the accounts about motivation was more differentiated: Though it was
expressed in both, the questionnaires and the interviews, that the authors' intrinsic
motivation was quite high (for example they expressed that the interaction with the
program was fun), it became clear in three of the interviews that the authors would
also appreciate an extrinsic motivation to produce a case — for example the
possibility to publish learning cases. At the very least, it is seen as necessary to get
time off from the clinical work for the duration of the case production.
Difficulties with the technical interaction and necessary supports. The quality of the
user interface and the usability of the program were assessed by the subjects for the
most part positively. In two interviews the drag and drop technique in particular were
described as very intuitive and easy to learn. This positive assessment is in agreement
with the analysis of the case production: Difficulties in the utilization of the program
barely arose while producing a case with CASUS. Accordingly, there were few places
where the observers had to intervene for the purpose of giving technical support. Also
in the subsequent interviews, the suggestions of changes and supports in a technical
sense were few and easy to realize (e.g. easier printing of screens).
Difficulties with the realization of the didactic concept and necessary supports. The
application of problem-based learning as an instructional concept in the production of
ZSfHD 3/99
2
a case turned out to be much more problematic. The handbook was considered very
comprehensible by all the authors, and they believed also that with the help of the
handbook they had understood the basic idea of problem-based learning; however,
during the production of the case many questions and difficulties arose concerning
preparing the medical content in the design of a case. One main difficulty which all
four subjects experienced throughout the production of a case was in dealing with the
learning objectives. For one thing it was troublesome for the authors to formulate
learning objectives based on the case. For another, they often only succeeded with
coaching from the observer in designing a didactic unit or screen from the viewpoint
of a specific learning objective. Two subjects also required significant support with
the design of differential-diagnostic networks; above all, the case authors had the
difficulty here of choosing relevant findings and suspected diagnoses appropriate to
the target group. Furthermore, three of the subjects were barely capable of choosing
and designing appropriate interactive elements without support (e.g. question and
answer sequences, cards with multimedial components). An analysis of the points
where intervention was necessary for the instructional design can be summed up as
follows: Almost all interventions for instructional design were directly related to the
content of the specific case. Standardized instructional support for the authors —
whether in a handbook or as online help — would have been of use with very few of
the difficulties. The problems the authors had were not instructional, but rather a
mixture between the specific content of the case and questions how to transfer these
content into a case-based learning program.
Keywords: Mapping-Tool, Case-based Learning, Differential Diagnosis, Hypothetical
Deductive Process, CASUS-Lernsystem
Einleitung:
Der
Einsatz
von
zur
Concept-Mapping-Techniken
Unterstützung
des
Problemlöseprozesses ist in verschiedenen Domänen etabliert [1]. Fischer et al.
konnten
für
die
Medizin
anhand
eines
fallbasierten
Lernsystems
zur
Differentialdiagnose von Anämien [2] zeigen, das ein einfach zu bedienendes
Mapping-Werkzeug zu einer signifikanten Zunahme der gebildeten diagnostischen
Hypothesen und der Verknüpfungen der Hypothesen mit klinischen Befunden bei
Medizinstudenten im klinischen Studienabschnitt führt: Dabei profitierten diejenigen
Studenten
mit
dem
höchsten
Vorwissen
am
meisten
von
dieser
Visualisierungsmöglichkeit. Darüberhinaus zeigte sich ein nicht-signifikanter Trend
zu mehr korrekten Hypothesen bei der Studentengruppe, die das Mapping-Werkzeug
Concept-mappping techniques support the hypothetical-deductive problem solving
process. In medicine through the use of these techniques an improvement in both
quantity and quality of diagnostic hypotheses can be demonstrated. The case-based
learning system CASUS provides a mapping-tool for the visualization of medical
differential diagnoses, which employs drag&drop interactivity to connect the critical
findings of a learning case to the respective hypothesis by weighed links. We
evaluated the use of the CASUS-mapping component in 6 internal medicine cases in
the 3rd clinical semester at the University of Munich: 439 case sessions were recorded
yielding 265 differential diagnostic networks with at least two hypothesis (60.3%).
This paper describes the characteristics of the student differentials and compares
them with the solutions derived from experts. Further it provides perspectives for the
use of this data in future feedback processes.
benutzte. Das CASUS-Lernsystem [3] erlaubt die rasche Entwicklung von
fallbasierten Lerneinheiten für Studierende der Medizin. Die Fallautoren benötigen
keine Programmierkenntnisse und werden didaktisch bei der Fallstrukturierung
unterstützt. Die Fälle sind linear strukturiert und werden von den Studierenden
Bildschirmkarte für Bildschirmkarte bearbeitet. Bei der Bearbeitung müssen klinische
Informationen (sog. Befunde) gesammelt und in Frage-Antwort Dialogen interpretiert
werden. Ein Fall gliedert sich in didaktische Einheiten, für die jeweils explizite
Lernziele von den Fallautoren formuliert werden. Am Ende jeder didaktischen Einheit
eines Falles wird ein Mapping-Werkzeug aufgerufen, mit dem die Befunde per
Drag&Drop-Interaktivität mit diagnostischen Hypothesen in Beziehung gesetzt
werden sollen. Der Studierende kann das eigene differentialdiagnostische Netzwerk
mit dem des klinischen Experten vergleichen.
In dieser Arbeit wird die Evaluation der von Studierenden im klinischen
Studienabschnitt erstellten differentialdiagnostischen Netzwerke dargestellt. Welche
Bedeutung hat eine tutorielle Betreuung für die Benutzung des Mapping-Werkzeugs?
Welche weiteren Entwicklungs- und Evaluationsschritte sind erforderlich, um solche
Werkzeuge im Feedbackprozess zwischen Lehrenden und Lernenden nutzbringend
einzusetzen?
Methoden:
Das Mapping-Werkzeug zur Erstellung differentialdiagnostischer Netzwerke wurde
1996 mit der Object Modelling Technic von J. Rumbaugh entworfen. Die
Implementierung erfolgte mit der Entwicklungsumgebung Metrowerks CodeWarrior®
in der Programmiersprache C++. Die Daten werden in einer relationalen Datenbank
(ORACLE 8.0.5) abgelegt [4]. Die dieser Arbeit zu Grunde liegenden Auswertungen
erfolgten über SQL-Anweisungen mit dem graphischen Datenbank-Explorer
Voyant®.
Abb. 1: Expertennetzwerk des CASUS-Lernfalles zum Thema "Obere
gastrointestinale Blutung": Die blauen Elemente repräsentieren klinische
Befunde, die diagnostischen Hypothesen sind rot und Therapien grün
dargestellt.
In Abbildung 1 ist exemplarisch das Netzwerk eines Experten dargestellt. Die blau
eingefärbten Befundelemente werden der zeitlichen Abfolge des Lernfalles
entsprechend Schritt für Schritt per Drag&Drop auf die Arbeitsfläche positioniert.
1
ZSfHD 3/99
ZSfHD 3/99
2
Dabei läßt sich des differentialdiagnostische Netzwerk jederzeit "zurückblättern"
Abb. 2: Dialogfenster bei der Erstellung einer neuen Zuordnung zwischen
klinischem Befund und Hypothese: Positive Verknüpfungen sind in drei Stufen
in blau, negative in rot auswählbar. Jede Verknüpfung sollte vom Fallautor und
dem Studierenden begründet werden. Wenn verfügbar, lassen sich Daten zu
Prävalenz, Sensitivität und Spezifität eines Befundes im Bezug auf die
entsprechende Hypothese eingeben.
(Pfeile unterhalb der Arbeitsfläche in Abbildung 1). In rot dargestellte Hypothesen
werden ebenfalls per Drag&Drop neu erstellt und mit gewichteten Verbindungen zu
den entsprechenden Befunden versehen. Die blauen Linien stellen dabei bestätigende
und die roten Linien widerlegende Verknüpfungen dar. Es stehen jeweils drei
Verbindungsstufen zur Verfügung.
Die statistische Analyse der Nutzerdaten zur Erstellung der differentialdiagnostischen
Netzwerke wurde mit dem Chi-Quadrat Test in StatView®durchgeführt.
Ergebnisse:
Wir evaluierten Studierende aus dem 3. klinischen Semester der Ludwig-MaximiliansUniversität München. Zu sechs CASUS-Lernfällen aus der Inneren Medizin konnten
wir 439 Lernsitzungen aufzeichnen. Die Fallbearbeitung lag zwischen 60 und 90
Minuten pro Lernfall. Die Lernfälle wurden vorlesungsbegleitend und abgestimmt auf
die Vorlesungsthemen angeboten und sequentiell alle 10 bis 14 Tage im Semester
freigeschaltet. Dabei wiesen 265 Logfiles zu den einzelnen Fallbearbeitungen
differentialdiagnostische Netzwerke (60,3%) mit mehr als 2 Hypothesen auf und
wurden als relevant angesehen und ausgewertet. Fall 1 (siehe Tabelle 1) wies den
höchsten Anteil auswertbarer Daten auf, weil dieser Fall unter tutorieller Anleitung
bearbeitet und als Testat für die Vorlesung Innere Medizin angerechnet wurde. Zwei
weitere frei wählbare Fälle qualifizierten die Studierenden für ein weiteres Testat. Die
Fälle 2 bis 6 wurden im Selbststudium ohne Anleitung bearbeitet.
1
2
3
4
5
6
Gesamt
Fallbearbeitungen
119
103
63
102
40
12
439
Anzahl auswertbarer
Netzwerke (%)
109
51
25
59
16
5
265
Fallnummer
(91,6)
(49,5)
(39,7)
(57,8)
(40)
(41,7)
(60,3)
ypothesen pro Fall
2,8
3,2
2,5
2,8
2,3
3,0
2,82
erknüpfungen
efund-Hypothese
9,4
8,7
5,8
9,2
5,3
14,6
8,69
3,4
2,7
2,3
3,3
2,3
4,9
3,08
erknüpfungen
ypothese
pro
nur ein Teil der Studierenden ein Netzwerk. Die Qualität der Netzwerke unterscheidet
sich unter beiden Bedingungen nicht.
Die Netzwerkdaten der klinischen Experten wurden bereits MeSH-codiert und
sind somit inhaltlich eindeutig zuzuordnen. In einem nächsten Evaluationsschritt
werden die Netzwerkdaten der Studierenden ebenfalls qualitativ mithilfe eines
halbautomatischen MeSH-Codiersystems [5] analysiert und mit den Daten der
Experten verglichen. Diese Vergleichsdaten sollen dann in die Feedbackkomponente
Tabelle 1: Quantitative Auswertung der differentialdiagnostischen Netzwerke
der Studierenden; Fall 1 mit tutorieller Unterstützung, die Fälle 2 bis 5 im
Selbststudium.
des CASUS-Lernsystems integriert werden. Ein Dozent kann die diagnostischen
Unstimmigkeiten zwischen dem Vorgehen des Experten und dem der Studierenden als
Basis
Die Daten zeigen, daß die Anzahl der Fallbearbeitungen offensichtlich stark von
der Relevanz der Fälle für die Anrechnung eines Testates beeinflußt wurde. Die
Anzahl auswertbarer Netzwerke wurde wesentlich von der Anwesenheit eines Tutors
für
eine
Diskussion
mit
den
Studierenden
verwenden.
Ob
das
differentialdiagnostische Netzwerk des Experten den Studierenden erst am Ende eines
Falles gezeigt werden sollte und welchen Einfluß dies auf die Quantität und Qualität
der Netzwerke hat, wird eine weitere Evaluation zeigen.
beeinflußt, der die Handhabung des Werkzeuges erklärte. Im Selbststudium bei den
Fällen 2 bis 5 erstellten signifikant weniger Studierende ein Netzwerk (p < 0,01). Die
Anzahl der generierten Hypothesen unterschied sich dagegen im Vergleich der
einzelnen Fälle nicht signifikant voneinander. Für die Anzahl der Verknüpfungen
zwischen Befunden und Hypothesen und die Anzahl der Verknüpfungen pro
Hypothese ergab sich nur für den sechsten Fall ein signifikanter Unterschied (p <
0,05). Dieser Fall wurde allerdings nur von zwölf Studenten bearbeitet.
Möglicherweise handelt es sich um eine hochmotivierte Untergruppe von Studenten,
die nicht repräsentativ sind. Dafür spricht die Tatsache, daß sie am Semesterende vor
den Abschlußklausuren einen Fall ohne Testatrelevanz bearbeiteten.
Zusammenfassung:
Das
Mapping-Werkzeug
Hypothesenbildung
im
zur
Unterstützung
CASUS-Lernsystem
der
differentialdiagnostischen
weist
unter
tutoriellen
Betreuungsbedingungen eine hohe Akzeptanz auf. Im Selbststudium erstellt dagegen
1
ZSfHD 3/99
2.4
Discussion
With the authoring system CASUS physicians can produce learning cases even
without any special prior knowledge with regard to technical aspects. The technical
difficulties which arose during the formative evaluation of the authoring system could
be taken into consideration without too much trouble when the program was revised.
The handbook and the online help appear — based on these results — to be sufficient
to use CASUS for the production of cases with little technical effort. Altogether, it can
be expected that authors who have used other computer applications (and who
otherwise have no great computer knowledge) can learn the technical operation of the
program quickly and without difficulty.
In the didactical respect, there were far more problems with the production of cases.
Only a few of the difficulties encountered can be addressed in the handbook or online
help since the problems were very specific to the content of individual cases. This
leads to the conclusion that it is at least helpful if not necessary to provide the authors
with support in the didactical aspect of the content. At the very least, this is the case
with authors who do not yet have comprehensive experience in problem-based
teaching. This result is in agreement with findings on the introduction of problembased curricula: In evaluation studies it was shown that instructors felt themselves to
be not up to the task of teaching a problem-based course, and they desired more
support (Albanese & Mitchell, 1993). Because of the experiences in the formative
evaluation study, it seems that an appropriate measure for the CASUS authors would
be a tutor model. Close cooperation between the author and a person who
ZSfHD 3/99
2
demonstrates expertise in both a professional and instructional sense appears to be a
good way to ensure the quality of the learning cases.
The development of a guide for tutoring of authors is only one of the goals which will
be pursued within the scope of this project in the near future. The emphasis will now
be placed on using the authoring system CASUS to produce suitable learning cases for
different content-areas and levels of study. This is connected with investigations into
the effectiveness of learning with CASUS cases. Thus far, an initial evaluation with a
prototypical case was performed only to determine the acceptance and learning
motivation among students. It was shown that the students generally worked on the
case with a high level of motivation and most expressed the wish that cases of this
type would be better integrated in the course of studies. This judgement of the learning
cases proved to be independent of the technical competence of the students. In further
investigations the question will be pursued on the basis of constructivist approaches,
to what extent computer-based learning cases are suitable for imparting coherent and
application-oriented knowledge. An emphasis will be placed on finding differentiated
quality characteristics of learning cases on a theoretical and empirical basis. With
these subsequent empirical studies and the efforts to implement the existing cases at
different universities, it will be attempted to contribute to the improvement of medical
education. An important field of application is the supplementation of clinical courses
with key symptoms and diseases in the case format. As an integral part of a course, the
cases will ensure the even exposure of students to these problems when there is no real
patient with the respective problem available.
References
series as a case study. In T. M. Duffy, J. Lowyck, D. H. Jonassen &
T. M. Welsh (Eds.), Designing environments for constructive
learning (pp. 9-36). Berlin: Springer.
ACME-TRI Report (1993). Educating medical students.
Collins, A., Brown, J. S. & Newman, S. E. (1989). Cognitive
Assessing change in medical education - the road to
apprenticeship: Teaching the crafts of reading, writing, and
implementation. Academic Medicine, 68 (6, Supplement), 1-48.
mathematics. In L. B. Resnick (Ed.), Knowing, learning, and
Albanese, M. A. & Mitchell, S. (1993). Problem-based
learning: A review of literature on its outcomes and implementation
issues. Academic Medicine, 68, 52-81.
Armstrong, E. G. (1997). A Hybrid Model of Problem-based
instruction. Essays in the honour of Robert Glaser (pp. 453-494).
Hillsdale: Erlbaum.
Das Arztbild der Zukunft (1993). Analysen künftiger
Anforderungen an den Arzt - Konsequenzen für die Ausbildung und
Learning. In D. Bound & G. Feletti (Eds.) The Challenge of
Wege zu ihrer Reform. [Analysis of future standards for physicians.
Problem-Based Learning (pp. 137-150). London: Kogan Page.
Consequences for the reform of medical education]. Gerlingen:
Barrows, H. S. (1985). How to design a problem-based
curriculum for the preclinical years. New York: Springer.
Barrows, H. S. & Tamblyn, R. (1980). Problem-based
learning. New York: Springer.
Cognition and Technology Group at Vanderbilt (1992). The
Jasper series as an example of anchored instruction: Theory,
Blecher.
Duffy, T. M. & Jonassen, D. H. (1992). Constructivism:
New implications for instructional technology. In T. M. Duffy & D.
H. Jonassen (Eds.), Constructivism and the technology of
instruction: A conversation (pp. 1-16). Hillsdale: Erlbaum.
Fischer, F. (1998). Mappingverfahren als Werkzeug für das
program, description, and assessment data. Educational
problemorientierte Lernen. Entwicklung und empirische
Psychologist, 27, 291-315.
Untersuchung eines computerbasierten Mappingverfahrens in der
Cognition and Technology Group at Vanderbilt (1993).
Domäne Medizin [Concept mapping as a tool for problem-oriented
Designing learning environments that support thinking: The Jasper
1
ZSfHD 3/99
ZSfHD 3/99
2
learning. Development and evaluation of a computer-based mapping
oriented learning. Strategy use and possibilities for design].
technique]. Frankfurt: Lang.
Göttingen: Hogrefe.
Fischer, F., Gräsel, C., Kittel, A. & Mandl, H. (1996).
Gräsel, C. & Mandl, H. (1993). Förderung des Erwerbs
Entwicklung und Untersuchung eines computerbasierten
diagnostischer Strategien in fallbasierten Lernumgebungen.
Mappingverfahrens zur Strukturierung komplexer Information. [A
[Fostering the acquisition of diagnostic strategies in case-based
computer-based mapping technique for structuring complex
learning environments]. Unterrichtswissenschaft, 21, 355-370.
information: Development and some preliminary data]. Psychologie
in Erziehung und Unterricht, 43, 266-280.
Fischer, M. R. G., Schauer, S., Gräsel, C., Baehring, T.,
Leutner, D. (1992). Adaptive Lehrsysteme. [Adaptive
systems for learning]. Weinheim: Psychologie Verlags Union.
Moore, G. T., Block, S. D., Style, C. B. & Mitchell, R.
Mandl, H., Gärtner, R., Scherbaum, W. & Scriba, P. C. (1996).
(1994). The influence of the New Pathway curriculum on Harvard
Modellversuch CASUS - ein computergestütztes Autorensystem für
Medical students. Academic Medicine, 69, 983-989.
die problemorientierte Lehre in der Medizin. [CASUS - a computer-
Stark, R., Gruber, H., Renkl, A. & Mandl, H. (1998).
based authoring system for problem-based learning in Medicine].
Instructional effects in complex learning: Do objective and
Zeitschrift für ärtzliche Fortbildung, 90, 385-389.
subjective learning outcomes converge? Learning and Instruction,
Gijselaers, W. H. & Schmidt, H. G. (1990). Development
and evaluation of a causal model of problem-based learning. In A.
8, 117-129.
Tosteson, D. C., Adelstein, S. J. & Carver, S. T. (Eds).
M. Nooman, H. G. Schmidt & E. S. Ezzat (Eds.), Innovation in
(1994). New Pathways to Medical Education: Learning to Learn at
medical education: An evaluation of its present status (pp. 95-113).
Harvard Medical School. Cambridge: Harvard University Press.
New York: Springer.
Gräsel, C. (1997). Problemorientiertes Lernen.
Strategieanwendung und Gestaltungsmöglichkeiten. [Problem-
Williams, S. M. (1992). Putting case-based instruction into
context: Examples from legal and medical education. The Journal of
the Learning Sciences, 2, 367-427.
1. Fischer F: Mappingverfahren als kognitive Werkzeuge für
problemorientiertes
Lernen.
Europäischer
Verlag
der
Wissenschaften (Frankfurt am Main). 1998.
2. Fischer F, Gräsel C, Kittel A & Mandl H: Entwicklung und
Untersuchung eines computerbasierten Mappingverfahrens zur
Strukturierung
komplexer
Information.
Psychologie
in
Erziehung und Unterricht. 1996; 43:266-280.
3. Fischer MR, Schauer S, Gräsel C, et al.: Modellversuch
CASUS. Ein computergestütztes Autorensystem für die
Problemorientierte Lehre in der Medizin. Zeitschrift für
Ärztliche Fortbildung. 1996; 90:385-9.
4. Adler M: Entwicklung einer differentialdiagnostischen
Netzwerk-Komponente für die medizinische multimediale
Lerndatenbank
CASUS.
Diplomarbeit
am
Institut
für
Informatik der Technischen Universität München. 1996.
5. Holzer M F, Dietrich JW & Fischer MR: Freitextverarbeitung und
Verschlagwortung in medizinischen Lernprogrammen: Ein Vergleich von
SNOMED und MeSH. In: Methoden der Medizinischen Informatik,
Biometrie und Epidemiologie in der modernen Informationsgesellschaft.
E. Greiser und M. Wischnewsky (Hrsg). 1998. München, MMV: 282-6.
1
ZSfHD 3/99
ZSfHD 3/99
2