Web-based education in bioprocessengineeringO.D.T. Sessink1, H. van der Schaaf1, H.H. Beeftink1, R.J.M. Hartog2 and J. Tramper1

1 Food and Bioprocess Engineering Group2 Wageningen Multimedia Research Center, PO Box 8129 6700EV, Wageningen University, The Netherlands

Review TRENDS in Biotechnology Vol.25 No.1

The combination of web technology, knowledge ofbioprocess engineering, and theories on learning andinstruction might yield innovative learning material forbioprocess engineering. In this article, an overview ofthe characteristics of web-based learning material isgiven, as well as guidelines for the design of learningmaterial from theories of learning and instruction andfrom the bioprocess engineering domain. A diverse bodyof learning material is presented, which illustrates theapplication of these guidelines; this material has beendeveloped during the past six years for different courses,mostly at undergraduate level, and it illustrates howweb-based learning material can enable various differentapproaches to learning objectives that might improveoverall learning. Such learning material has been usedfor several years in education, it has been evaluated withpositive results, and is now part of the regular learningmaterial for bioprocess engineering at Wageningen Uni-versity.

IntroductionThe landscape of academic learning is changing since themass adoption of the internet in education. Most of thesechanges have addressed communication and distribution;however, web technology enables new approaches for con-tent that might change the way future learning material(LM) is designed and used [1,2].

In this article we argue how the combination ofweb-technology, knowledge of bioprocess engineeringand theories for learning and instruction might yield inno-vative bioprocess engineering LM. We first give an over-view of the characteristics of web-based LM; then wepresent guidelines for the design of web-based LM [3];finally, a variety of web-based LM modules is presented,to illustrate these guidelines and to show the variousapproaches that are facilitated by web-technology. Thematerial is available online (http://fbt.wur.nl/; choose ‘Con-tent showcase’ and then select any of the listed processengineering modules).

Characteristics of web-based learning materialWeb-based LM is created with web-technology (Box 1) butis also a subset of digital LM; therefore, it has character-istics that are unique for web-based LMand characteristicsof digital LM in general.

Corresponding author: Beeftink, H.H. (rik.beeftink@wur.nl).Available online 17 November 2006.

www.sciencedirect.com 0167-7799/$ – see front matter � 2006 Elsevier Ltd. All rights reserve

(i) W

d. doi:10

eb-basedLMrequires only aweb-browser (InternetExplorer or Mozilla Firefox), which are available bydefault on most platforms, and an internet connec-tion, whereas digital LM, in general, might havemany more requirements.

(ii) W

eb-based LM is directly accessible by manystudents simultaneously, and is independent of timeand place. In contrast to digital LM that isdistributed on physical media (e.g. CD-ROM),students directly access the original source of thematerial and, therefore, the latest version of thatLM. Authors can update their LM during a course toincorporate feedback from students, even if thematerial is used in distance education.

(iii) A

lthough not unique to web-technology, the hyper-link is perhaps the most well known characteristicof web-technology. The hyperlink enables one pieceof web-based material to reference another piece ofweb-based material, such as LM from anotheruniversity, literature or an online encyclopedia.

(iv) D

igital LM, in general, permits various forms ofmultimedia and interaction. It might includeformatted text, drawings, photos, animations, simu-lations, video, interactive applications and commu-nication components. Web-based LM, in particular,offers various high-level components to build suchrich material, where using multimedia in LMmight,if used appropriately, improve learning effectiveness[4,5].

(v) D

igital LM enables interaction with the student, forexample, the LM can show a response to a studentaction, such asmouse or keyboard actions. One of themost important applications for interaction in LM isfeedback – the information about progress towards aparticular goal [6] – for which there are severaldesign guidelines available [7].

(vi) D

igital LM has access to computational resources,which can be used to provide simulations that canproduce output from student input in modelsystems. In a flight simulator, for example, theinputs are the handles and the control stick, andthe outputs are changes in the cockpit window andthe indicators. If LM enables only a few combina-tions of inputs, simulation is not required because allpossible outputs can be pre-calculated and includedin the material [8]; however, if a wide or unlimitedrange of student inputs are permitted, a simulationis appropriate [9,10]. The effect of input on output is

.1016/j.tibtech.2006.11.001

Box 1. What is web technology?

Web technology delivers information throughout the worldwide

web by way of a web browser. The information is produced or

retrieved by a web server and presented on the screen of the client

computer by a web browser, such as Microsoft Internet Explorer or

Mozilla Firefox. Web technology can be categorized by the computer

on which the processing takes place: client-side or server-side

technology.

Client-side technology is technology that is processed by the web

browser on the client computer. It is executed within a so-called

‘sand box’ in the web browser. This is a virtually isolated software

environment that, for security reasons, is limited in both its

interactions with other software as well as interactions with other

computers on the internet. Because of this sandbox, client-side

technology has few resources to store data; however, because

processing takes place on the client computer, the interaction

latency (the time between an action and a reaction) is low. Examples

of client-side technology include HTML, ECMAScript, Macromedia

FlashTM and Sun JavaTM.

Server-side technology is processed on the server and the results

are sent to the client. Because a single server usually serves many

clients, the computational resources are shared. A web server is

usually tightly linked with other applications, such as a content

management system and a database management system (DBMS).

Furthermore, the server environment can be customized for a

specific task. It can be configured, for example, such that processes

from different users can share data. Because actions have to be sent

to the server and reactions have to be sent back to the client, server-

side technology exhibits a high interaction latency. Examples of

server-side technology include PHPTM and TomcatTM.

Review TRENDS in Biotechnology Vol.25 No.1 17

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present in many learning objectives: from theoperation of equipment to the discovery of systembehavior. A learning objective might be the ability todetect anomalies, to identify signs of specificbehavior, to predict behavior or to predict a state,or to find the combination of inputs required toobtain a specific state or specific behavior [11].Simulations can be used to support a learningobjective in a way that would be impossible orunacceptable in reality – because of danger, costs,availability of equipment, time or irreversibledamage. Students might use simulations to practicesituations they might never meet during theireducation or to prepare for situations for whichaccess in education is limited [9,10]. Furthermore,simulations might exaggerate reality to providemore effective learning experiences [12], for exam-ple, accelerate time and thus enable students todiscover how a parameter affects long-term systembehaviour; or the simulation might confront thestudents with an important but infrequently occur-ring phenomenon; or the simulation might simplifyreality and assume ideal behavior.

(vii) W

eb-based material can offer advantages for colla-borative learning because collaborative learningneeds communication, and web-based LM canintegrate a communication component in the mate-rial.

(viii) D

igital LM, in general, can support a variety of tasksand, thus, might enable an integrated learningexperience [13]. An integrated learning experiencemeans that a learning environment enables stu-dents to engage in multiple tasks related to learning

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without a conscious switch between these tasks. In awell-integrated environment, for example, studentscan move their attention from a text to a video andthen to a communication component without thefeeling that their attention was required for any-thing other than learning.

(ix) D

igital LM can identify individual users, track theiractivities and build, retrieve and update a model ofthe user; therefore, it might permit adaptive LM.Web-technology, in particular, has many means forauthentication, tracking, and structured data sto-rage, and is thus well suited for implementation ofadaptive functionality. Adaptive LM builds a modelof the goals, preferences and knowledge of eachindividual user and uses this model throughout theinteraction with the user to adapt to their needs [14].Adaptive LM can, for example, present moredetailed and deep information to more qualifiedstudents or suggest a set of most relevant links toeach individual student. This might minimize userfloundering, make the learning more goal-orientedand make the learning more efficient [15].

Six guidelines for LM designThis section lists six guidelines that have been used for thedesign of LM; they will further be referred to as learningmaterial guideline (lg) 1–6.

lg1: LM should activate students

Learning is found to be more effective and yield betterresults if students participate actively [16,17] – LM thatstimulates students to put effort into learning is calledactivating LM. Digital assignments are well suited toactivate students: they can interact with students, displayhints, and return verification of correct student input.Experience at Wageningen University shows that morestudents are activated by digital LM than by traditionalassignments.

lg2: LM should impose only minimal cognitive load

Cognitive load theory assumes that learning is limited bythe total cognitive capacity of a student. For example,extraneous cognitive load caused by an ill-designed userinterface or by a non-native language should, thus, beminimized – many guidelines for user interface designto reduce extraneous cognitive load are available [18].Intrinsic cognitive load – the load that is directly relatedto the learning objectives – should match the level of thestudents and the time available to work with the LM [19].Unnecessary cognitive load can also be avoided by visualrepresentation, the use of which is facilitated by webtechnology.

lg3: LM should be modular by design

ModularlydesignedLMaddressesonlyoneora few learningobjectives and requires little of the unnecessary prere-quisite knowledge that would exclude some students fromeffective learning. For example, LM about enzymekinetics should not require knowledge of protein struc-tures and composition. Furthermore, feedback is moreeffective when triggered in a specific situation: the more

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determinants an answer has, the less effective thefeedback will be [7].

lg4: LM should provide authentic context and tasks

A range of theories on learning and instruction assumethat authentic contexts and authentic assignments areessential for effective learning. Thus, biotechnology stu-dents should learn about mixing in the context of cellcultivation instead of, say, paint mixing. Correspondingly,a task for mixing should be to calculate oxygen transferinstead of calculating liquid velocity. An interactive simu-lation of a real system is an accepted way to provide anauthentic context [20]. In practice, authenticity can conflictwith cognitive load guidelines [20] and, in such cases, it isthe opinion of the authors that the latter should haveprecedence.

lg5: LM should use personalized support where possible

Academic courses are typically attended by a heterogeneousstudent population. The sameLMwill not be effective to thesame degree for every student in the population. Adaptivematerial automatically changes to the needs of the users,based on assumptions made by the system about studentrequirements.

lg6: LM should be motivating

Attention, relevance, confidence and satisfaction (ARCS)determine a set of guidelines for motivation [21]. First, LMshould capture the attention of the student; second, itshould be relevant for the student; third, the studentshould be confident that he or she can handle the assign-ment; and last, the material should stimulate a sense ofsuccess and satisfaction.

Guidelines for bioprocess engineering educationIn this section, four bioprocess engineering guidelines (bg)for LM that have been used are listed. These guidelines

Table 1. Example assignments with different aspects of the conne

Mechanism/

phenomenon

Mathematical

representation

Qualit

quant

system

behav

Estimate parameter values for

a given model from given

experimental data.

X

Choose the most appropriate

mathematical representation

given a set of experimental

data and a set of possible

mechanisms

X X X

Reason the behavior of a system

after a given parameter change.

X

Adapt a mathematical

representation to include

some assumption.

X

Calculate a steady state given a

mathematical model and

parameter values

X X

Give the model domain for a

model and its

underlying assumptions

X

Choose a mathematical

representation for a system

described in natural language.

X X

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are derived from the bioprocess engineering learningobjectives at Wageningen University.

bg1: LM should balance quantitative and qualitative

practice

An experiment by McDermott [22], in which a large groupof undergraduate students with adequate quantitativeskills in using Ohm’s law failed on an assignment to makequalitative inferences based on Ohm’s law, showed thatsuccess on quantitative exercises implies no evidence forqualitative understanding. Students should thus practicequalitative reasoning in parallel with quantitative exer-cises.

bg2: LM should use multiple ways to connect model

theory to physical reality

Many bioprocess engineering textbooks featureassignments in which students are asked to make a givencalculation, for example, m = 0.1 h�1 without any verbalreference to specific growth rate. Numerically, such anassignment might not be completed satisfactorily withoutany conceptual understanding. There are, however, moreaspects of the connectionbetweenmodel theoryandphysicalreality, and students should be confronted with all of them.Table 1 lists example assignments with different aspects ofthe connection between model theory and physical reality.

bg3: LM should enable students to practice design skills

Design assignments state objectives, specify requirementsand enable the student to combine methodical steps andpersonal decisions to achieve a result that meets theseobjectives and requirements (adapted from [23]). Becauseeach design is unique, grading is time consuming; further-more, students find it difficult to judge their own resultsand require much assistance. Even experiment design israrely done by students themselves; in practical courses,students often follow a recipe [24,25].

ction between model theory and physical reality

ative or

itative

ioue

Natural

language

representation

Model

domain

(valid input

range)

Operational

definition of

symbols

Assumptions

X

X

X X

X

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Design can be practiced with web-based LM. This LMcan assist the student with frequently occurring problems.It might, for example, force a step-by-step structuredapproach, in which every step is checked for compliancewith previous steps. Students can also learn from simula-tions that show how their design is behaving.

bg4: LM should confront students with uncertainty in

parameters and models

Students, particularly undergraduates, falsely assumethat models and parameter values are perfect and areseldom confronted with uncertainty [9]. Many textbooksdo not show the uncertainty in parameter values, nor dothey mention the limited applicability of models. Withrespect to a model, LM should notify students what therelevant numerical ranges of its input variables are, towhat degree the system, in reality, exhibits ideal behavior,how large the uncertainty in parameters and variables is,and how sensitive the system is to changes in both. Whenlearning how to design new models, students should beinformed that models with parameters that cannot bemeasured accurately, or not at all, have a limited applic-ability.

Case-based learning materialIn the developed case-based LM, students are set ascenario in which they have to solve some problem, thecontext and task of which are chosen from real problemsdescribed in the literature or by industry. These are themost authentic of all designed material (lg4), and studentsfind them interesting and relevant (lg6). Students proceedthrough the scenario by answering a series of activatingclosed questions (lg1), and receive slightly personalized(lg5) feedback on their answers. The more tries the stu-dents need to answer the questions, the more feedback isgiven and the more concrete the hints in the feedback are:the questions and feedback guide students through thescenario. This approach borrows various aspects fromadventure games, such as the storyline, role-play andthe discovery of new information.

Several case-basedmodules, such as ‘mixing and oxygentransfer’, ‘membranes’ and ‘heat transfer’ have been

Figure 1. The flow of action in the adaptive system with closed questions. The grey ar

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designed for undergraduate courses. Generally, thesecases start with qualitative questions and slowly moveto more quantitative questions (bg1). Students do notpractice design; instead, all students follow the same so-called ‘closed’ questions and therefore find the same result.The ‘open’ nature of design, in which different answers tothe same question are obtained, is thus absent.

The subject is introduced in a regular lecture, followedby the modules, which are carried out in a computerlaboratory. Experience teaches us that little assistanceis asked for, and teaching assistants indicate they spendtheir time, effectively, with students who lack some pre-requisite knowledge or with students who have in-depthquestions. Yearly evaluation results indicate that studentslike these modules next to regular lectures and tutorials.

A framework for these modules that enables the quickcreation of a sequence of closed questions is designed. Thissupports five types of closed questions: multiple choice,multiple answer, numerical value, item selection andordering, and balance equation setup. These can beextended where required.

Adaptive learning materialOne module of adaptive LM has been designed for ‘cellgrowth kinetics in reactors’. In this material, each studentfollows an individual, personalized path through a collec-tion of questions (lg5), where each student will get the‘most appropriate’ questions to answer, selected accordingto their history in the system [26]. An outline of thisselection procedure is given in Figure 1. For example, astudent who can immediately select the correct definitionof the biomass yield might get more in-depth questionsabout biomass yield, whereas a student who needs multi-ple tries before selecting the correct definition will getmorequestions on the definition of biomass yield before any in-depth questions. Other criteria for question selectionsenable variation in topics between subsequent questions,variation in questions between different students workingat the same time, and matching to the confidence level ofthe students (lg6).

The questions should be mostly independent from otherquestions and thus modularly designed (lg3), such that the

ea involves student interaction.

Figure 2. Screenshot with experiment results from a batch experiment. Students take samples and get their results, which include experimental error.

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adaptive system has great choice when choosing the nextquestion. However, some questions might require prere-quisite knowledge; these are only presented after studentshave acquired this prerequisite knowledge by workingthrough a group of other questions.

The questions in the module about ‘cell growth kineticsin reactors’ are similar to the questions described in thecase-based LM: they are activating questions (lg1), withthe same staged interactive feedback (lg5); students do notpractice design; and many questions in this module acti-vate qualitative reasoning (bg1). Furthermore, the ques-tions confront students with many different aspects of theconnection between model theory and physical reality(bg2). The module does not address guideline lg4 at all,neither does it present an authentic context nor an authen-tic task.

Students are asked to finish the module individually,before the regular lectures. Students indicated that theyliked themodule by rating its usefulness as 4.4 on a scale of1–5. Students indicated that this LM is effective and theyliked the qualitative questions and the questions thatconnect model theory and physical reality. Compared withprevious years without this material, the lecturer observedthat students had fewer problems with introductory the-ory.

A framework for adaptive LM has been designed thatenables the quick creation of these adaptive modules. It isbased on the framework for case-based LM and adds aconceptually simple interface to specify the required infor-mation to use these questions in an adaptive sequence [26].

Experiment design and parameter estimation learningmaterialIn the LM developed for experiment design and parameterestimation, students practice the design of experiments for

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parameter estimation, execute these experiments in areactor simulation, and use the results for parameterestimation [9]. Figure 2 shows the virtual environmentin which students obtain samples with simulated numer-ical data. The results include experimental error and theoperational limits of analysis equipment, which are impor-tant constraints in experiment design. The task forcesstudents to focus on model- and experiment-related learn-ing objectives, which often receive little attention in reallaboratory experiments [24,25]. Experiments are simu-lated with accelerated time, which enables students topractice and execute many designs in a short time andlearn from their choices.

Experiment design for parameter estimation is anactivating (lg1) and authentic (lg4) task for students, whichthey find relevant (lg6). The LM does not provide anauthentic context, as described in guideline lg4; only alaboratory would. The material, however, avoids cognitiveoverload (lg2), whereas in a real laboratory, students areoverwhelmed with the number of practical details and failto focus on design and estimation issues. The materialenables students to practice the design process itself (bg3)(in an open way without much guidance), qualitative rea-soning (bg1) and reconstruction of their mental model ofthe system. Students are confronted with the uncertaintyin parameters because they will find uncertainty in theirown estimates (bg4), and by estimation of such parameters,students assign physical meaning to model theory them-selves (bg2). The material is modularly designed withrespect to parameter estimation (lg3) and does neitherinclude nor require a specific parameter estimationmethod, which enables it to be used in multiple courses.

The LM is used in the undergraduate coursesIntroduction to Process Engineering and Bioprocess Engi-neering; and despite following several practical courses

Review TRENDS in Biotechnology Vol.25 No.1 21

before, students indicate that real experiment design ismostly new to them. However, students quickly learn fromtheir first designs and continuously improve them: theyrate the usefulness of the material at 3.9 on a 1 to 5 scale.

The developed material includes various kinetic modelsbut is restricted to cell growth experiments in batch orCSTR reactors. Furthermore, a collection of Java compo-nents, which can be used to create other virtual experi-ments, has been developed for this material.

Downstream process design materialIn the developed LM for downstream process design,students design a process chain that meets certain purity,yield and cost requirements. This material is used in theBSc Bioprocess Engineering course. Students see an over-view of their design: the fixed composition of the starting

Figure 3. A screenshot of the downstream process designer, showing the performance

and operational settings can be changed.

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material, the variable composition of the final purifiedproduct, and the performance of each unit operation inbetween [27]. Students can insert and delete unit opera-tions or change operational settings for each unit operation(Figure 3). To activate students, the performance of eachunit operation and the content of the final product imme-diately reflect those changes (lg1). However, to avoid acognitive overload, the number of unit operations as well asthe number of operational parameters are kept low (lg2).This also enables students to discover better the qualita-tive relation between the parameters and the performance(bg1).

The design task is an authentic one (lg4). Severaladvanced design applications exist, which would providea more authentic context (e.g. SuperPro DesignerTM andAspen PlusTM); however, none of these applications is

of a small downstream process chain. Unit operations can be inserted or removed,

22 Review TRENDS in Biotechnology Vol.25 No.1

suitable for education early in the BSc course because theyhave a long learning curve.

Students are free to design any process chain thatmeetsthe design requirements – they are not guided to a specificanswer (bg3). After students have created a first design, anoverview is given of how their design relates to designsfrom other students. The designs that achieve the highestyield, the highest purity, the lowest costs, and the lowestwaste are shown. This demonstrates to students somefundamental design characteristics: different designs canall match the requirements, and different optimizationsare possible within the requirements. Furthermore, thiscomparison motivates students to improve their owndesign (lg6).

Students indicate this LM is both challenging and fun,and that they like the possibilities for discovering howparameters affect unit operation performance and howunit operation performance affects the total chain perfor-mance. Many students are motivated to optimize theirdesign to get it shown in the high score list. Assistantsindicate that they concentrate on their work and oftenengage in on-topic discussions.

The developed material can be extended with other unitoperations, or with unit operations with more operationalparameters. However, the functionality of the unit opera-tion has to be modeled on the basis of a few simple proper-ties of the stream, such as density, pI and size.

Systematic model design materialIn the developed material for systematic model design,students practice the design of a mathematical model toanswer an assignment [28]. Students engage in a largenumber of activities, grouped into four stages: generalanalysis, detailed analysis, model composition, and modelevaluation. The material guides students tightly throughthese stages but still enables students to design theirmodel freely.

In the designed material, students are asked to design amodel to predict when oxygen becomes limiting in anaerobic cultivation, in which the biomass concentration,substrate concentration and viscosity change. The predic-tion of oxygen limitation in a cultivation and the design of amathematical model are both considered to be activating(lg1), relevant (lg6) and authentic (lg4) tasks for students.There are many software applications that enable simula-tions of a model, such as MatlabTM or MathcadTM. Theseapplications, however, assume the model is already avail-able; none of them supports the model design process itself(bg3). Furthermore, these applications require a specificsyntax or user interface with a long learning curve, whichadds too much extra cognitive load for the students. Thestructured approach and the available interactive feedbackprovide guidance for students, reduce the cognitive load forthem (lg2) and give them the confidence to handle thedesign assignment (lg6). The material is activating: theinteractive questions continuously activate students toprovide an answer or to improve their existing answer(lg1). The LMmight help students to connect model theoryto physical reality: students first design a model for phy-sical reality, and then use this model to answer an assign-ment about physical reality (bg2).

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Students indicate that they like the structuring and unitchecking, and the fact that they can design amodel withoutgetting stuck in mathematical manipulation. This mate-rial is used at the end of the course. Students wouldappreciate it if such material were used more often inbioprocess engineering courses.

The developed material uses the closed questions fromthe case-based material framework and some Java-basedtools for model composition and numerical simulation.These tools can be used to create LM for any modeldescribed by differential equations. Furthermore, a collec-tion of Java components has been developed that can beused for simulation, unit checking and to search for steadystates.

DiscussionDigitalLMshouldcomplementotherLMwhereappropriate.It should better prepare students for lectures and tutorials,enable more effective interaction with lecturers and assis-tants during tutorials, enable students to practice morechallenging and motivating assignments and to engage inotherwise impossible learning activities.

The developed LM requires extensive databasefunctionality from the learning management system(LMS). Most current LMSs, such as MoodleTM or Black-boardTM, do not, at present, offer database functionality forlearning objects. New standards for LM that describe someof the required database functionality have already beendeveloped [29], and we expect that future standards willinclude adequate functionality.

To develop web-based LM that exploits all thepossibilities of web-technology, advanced knowledge ofboth the specific domain and of web technology are cur-rently required. We expect that the near future will bringhigh-level building blocks that are specifically designed asa basis to build LM. We also expect that applications forLM development that require less web-development skillswill become available. Together, this would make thedevelopment of web-based LM more accessible to authorswithout a web-development background. However, suchhigh-level components and user-friendly developmentapplications come at the price of reduced flexibility.

To some extent, the bioprocess engineering guidelineslisted above were a starting point for the development ofLM. Their final shape, however, emanated from the devel-opment, usage and evaluation of the various LMpresented.Experience acquired during several years has led us tobelieve that these guidelines are essential for bioprocessengineering education. Evaluation results of both tutorialsand exams in which guidelines bg1–bg4 were appliedindicate that students need more practice with LM thatimplements these guidelines.

The presented LMhas been designed during the past sixyears. In view of the guidelines presented in this article,there might be several opportunities for improvement. Alarge part of the developed material addresses learningobjectives that were applied atWageningen University butpreviously not supported by specific LM. All of the LM hasbeen used and evaluated, with positive results and satis-factory exam results, which indicates that the material isbetter than, or at least on par with, regular LM.

Review TRENDS in Biotechnology Vol.25 No.1 23

The developed material that supports previouslyunsupported learning objectives shows that evaluation ofnew technologies for LM in bioprocess engineering has alsoled to innovation in education in the subject. Perhaps mostimportantly, the development of new LM has stimulatedmany valuable discussions about the current bioprocessengineering education and bioprocess engineering LM atWageningen University.

ConclusionWeb-based LM is a useful addition for bioprocessengineering education. A combination of web-technology,general guidelines for LM and guidelines from the biopro-cess engineering domain enables various new approachesto learning objectives and has led to innovation in biopro-cess engineering education.

A variety of LM has been designed and developed forbioprocess engineering that might serve as an inspirationfor the development of new material. Most of the LM hasbeen used in education for several years as part of theregular LM for bioprocess engineering and has been eval-uated with positive results.

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eloping countries

rs launched the Health InterNetwork Access to

he world’s poorest countries to gain free or

rough the internet. Currently more than 70

providing access to over 2000 journals.

the WHO, said that this initiative was ‘‘perhaps

health information gap between rich and poor

s’’.

it www.who.int/hinari