Learner-experience design of eLearning systems in
neo-tropical animal production
Alexander Nikov
Department of Computing and IT
The University of the West Indies
Trinidad and Tobago
alexander.nikov@sta.uwi.edu
Stacey Syne
Department of Food Production
The University of the West Indies
Trinidad and Tobago
stacey.syne@sta.uwi.edu
Michele Singh
Department of Food Production
The University of the West Indies
Trinidad and Tobago
michele.singh@sta.uwi.edu
Gary Garcia
Department of Food Production
The University of the West Indies
Trinidad and Tobago
gary.garcia@sta.uwi.edu
Sasho Draganov
Department of Industrial Design Engineering
Technical University of Sofia
Bulgaria
sasho.draganov@tu-sofia.bg
Abstract: A framework for learner experience (LX) design of eLearning systems in neo-tropical
animal production is proposed. For assessment and design of learner experience a checklist is
developed. By a ridge-regression-based model the most critical of learning experience dimensions
and their checklist items are determined and relevant eLearning system improvements
recommended. A case study with an eLearning system in neo-tropical animal production is carried
out. By applying the framework the most critical for learner experience design checklist items and
problems are allocated. LX design guidelines and improvement recommendations are defined.
Further developments of the framework are suggested. By help of the framework important LX
dimensions and items can be quickly allocated and thus LX-oriented design of eLearning systems
in the area of neo-tropical animal production supported.
Introduction
Neo-tropical wildlife refers to terrestrial and aquatic non-domestic species that live in tropical areas of Latin
America and the Caribbean (Garcia, 2009). Terrestrial species such as the agouti (Dasyprocta leporina) and collared
peccary (Tayassu tajacu) play a vital role in the neo-tropics as a source of dietary protein and income for hunters
and rural communities (Ojasti, 1996). It was estimated that 150,000 kg of wild mammalian meat feeds more than
8,150,000 inhabitants of the Amazon forests annually and conservative estimates of game yields in rural Brazilian
Amazonia give a market value of US$190.7 million each year (Nogueira and Nogueira-Filho, 2011, Peres, 2000). In
Trinidad and Tobago these animals are of similar economic importance (Brown-Uddenberg, 2004). The number of
animals harvested during the hunting season between the years 1999 and 2003, progressively rose from 24,118 to
38,140 (Garcia, 2005). Additionally, the pricing structure of game yields has changed with time to reflect a steadily
increasing market value and demand. In 1995 wild meat sold for between US$5 and $11/kg, and this almost
quadrupled within a 9 year period to between US$18 and $33/kg in 2004 (Garcia, 2005).
Despite the intrinsic and market value of these species, there is a dearth of information on their behaviour,
reproduction and anatomy; and they are frequently subjected to unregulated hunting, dwindling habitats due to
deforestation, and neglect through improper forest management (Garcia, 2009, Peres, 2001). As a result, neotropical species are facing consequences such as reduced population biomass, reduced species diversity, and
possible extinction (Nogueira and Nogueira-Filho, 2011, Peres, 2001); making it crucial to implement initiatives that
focus on increasing populations (Alexandre, 2010, Brown-Uddenberg, 2004). One successful initiative is wildlife
farming or the rearing of non-domesticated animals for the purpose of captive breeding (Brown-Uddenberg, 2004,
Garcia, 2005, Nogueira and Nogueira-Filho, 2011). Programmes based on this have already taken place in French
Guyana with the collared peccary, and Brazil where the Universidad Estadual de Stanta Cruz has an active M.Sc.
Programme on neo-tropical wildlife species (Garcia, 2009, Garcia, 2005).
Similarly, in Trinidad and Tobago there has been emphasis on research and the utilization of neo-tropical
wildlife within The Open Tropical Forage-Animal Production Laboratory (OTF-APL) and the Open School of
Tropical Animal Science and Production (OSTASP) at the University of the West Indies, St. Augustine (Garcia,
2009). Several courses at both the under-graduate and post-graduate levels integrate neo-tropical animals into the
course syllabus alongside traditionally domesticated animals like dairy cattle and sheep (The Open School of
Tropical Animal Science and Production, 2012). Further to this, M.Sc. programmes such as Tropical Animal
Science and Production offer students a chance to delve deeper into neo-tropical animal wildlife management and
species production modeling.
To enhance and increase information dissemination to students, lecturers and members of the general public
interested in neo-tropical wildlife, a web-based eLearning system was created in 2004 for the OSTASP. The system
hosts information on neo-tropical animal species rarely available elsewhere without restrictions, and is geared
towards a broad spectrum of persons with varying academic backgrounds and experience. It has provided an
introduction for newcomers in the field of neo-tropical wildlife, as well as more in depth scientific publications for
university students.
Under-graduate and post-graduate lecture notes, course manuals and animal production manuals on the
system all function as necessary academic supplements to classroom learning. The system also has message boards
and chat rooms to enable networking and dialogue among website users in an effortless and timely manner. This
proves to be an essential aspect of teaching neo-tropical animal science as there is a heavy reliance on external
sources of information from hunters, farmers, zoos and researchers for case studies, theories and other information.
In actuality, the site can be considered as an eLearning platform as it integrates a number of tools and services for
teaching, learning, communication, and course management, all of which takes place independent of location
(Constabile, 2005, Zub and Eessaar, 2008).
In the past, the University of Florida (UFL) (Zarin, 2003) and the University of Copenhagen (Chomba and
Olsen, 2008) created similar eLearning platforms based on tropical forests. Like the OSTASP website, the basis of
the UFL proposal was a neo-tropical working forests website which would act as a public access resource for
information and facilitate communication among faculty, students and partner institutions. Unfortunately the site
could not be accessed and no information was lent on this venture’s best practices or lessons learnt. On the other
hand, the University of Copenhagen administered two online tropical forestry courses that carried with it advantages
of flexibility regarding the students’ time and geographical location, virtual classes with guest lecturers, and
knowledge exchange among an increased number of students (Chomba and Olsen, 2008). Some of the challenges
encountered with this teaching method were administrative huddles, repetition during discussions, copyright issues
and the scope for cheating during online tests (Chomba and Olsen, 2008). Apart from this, no usability testing of the
eLearning system was described and no studies have been found in the literature that pertains to eLearning systems
based on neo-tropical animal wildlife.
Very important for successful use of eLearning systems is their learner experience (LX) oriented design
(Garrett, 2010, Hartson and Pyla, 2012, Nielsen, 1993). Neglect of learner experience can be detrimental to both the
advancement of neo-tropical wildlife production and the academic success of students, considering that leaner
experience of an eLearning application can significantly affect learning (Constabile, 2005). Ideally, students should
spend more time learning academic material on a good eLearning interface rather than figuring out how to use it;
which, if ill designed with confusing menus or unclear buttons, can become a barrier to effective learning and
information retention (Constabile, 2007, Lanzilotti, 2006). Additionally, as the impact of information technology in
the teaching environment becomes more distinct (Chang, 2006), the site has potential to improve instruction. Course
instructors in the OSTASP have shown much enthusiasm for the site to provide an increased variety of academic
material such as “virtual field trips” to demonstrate neo-tropical animals in the wild. It is therefore imperative to
examine learner experience looking at components and key requirements of good learner experience in an online
learning environment. This can subsequently determine where and how improvements can be made.
It is critical for eLearning systems to embed strategies incorporating LX factors into the instructional design
process. In order to evaluate learner experience, Dringus and Cohen (2005) deemed student input as valuable and
highly recommended, thus enabling a more objective, focused and effective evaluation (Ardito, 2006). As
mentioned before, information on research on LX-oriented assessment and design of eLearning systems in neotropical animal production could not be found. LX-oriented design is critical for successful eLearning, and in this
paper a framework for LX design of eLearning systems in neo-tropical animal production is proposed. This
framework is illustrated by a case study with an eLearning system.
Framework Description
A framework for learner experience (LX) design of eLearning systems in neo-tropical animal production is
proposed (cf. Fig.1). At the first step of the framework the dimensions for LX assessment are determined. At the
second step LX checklist questions for data gathering (third step) for each dimension are determined. At the fourth
step a quantitative LX index LXI aggregating the three main LX components is calculated (cf. Fig. 2). For LX
assessment of the eLearning system a ridge regression model is employed. It uses the index LXI as a dependent
variable. By regression model the most critical LX dimensions and checklist items are determined (fifth step) and
recommendations for eLearning system design improvements (sixth step) are defined. In the following the
framework steps are described.
Determining of neo-tropical animal
production LX dimensions
1
Determining of LX checklist items
2
LX data gathering
3
LX-oriented assessment of eLearning system
4
Determining of most critical LX checklist
items and dimensions
5
Defining of LX design improvement
recommendations
6
Fig. 1 Framework steps
In order to determine the LX dimensions of the Neo-tropical animal production eLearning system a checklist
consisting of ten dimensions [measured by 43 checklist items (questions) as shown on Figure 2] was developed (first
step). The checklist questions are answered by the learner using the five-point Likert scale [1,5], ranging from
“Strongly Disagree” to “Strongly Agree” (second step). Low values close to 1 indicate good learner experience.
High values close to 5 indicate bad learner experience. Good learner experience (Anderson, 2011, Tullis and Albert,
2008) in an online learning environment has requirements of promoting interactivity and communication, easy
engagement in the instructional process (Dringus and Cohen, 2005), and should have characteristics such as
portability, efficiency, reliability and maintainability (Chua and Dyson, 2004). Navigation and consistency are
identified as key dimensions predominantly in need of improvement in online learning environments and are a
priority when measuring learner experience (Dringus and Cohen, 2005). Other dimensions such as sensory,
interface, visual, learning, and interaction design also played an important role with their associated aspects of
presentation, functionality and errors, hypermediality, memorability and course management respectively. Lastly,
interactivity and communication were also important (Constabile, 2005, Dringus and Cohen, 2005, Zub and Eessaar,
2008). Incorporating affective/emotive design and satisfaction into user experience studies are also important as they
describe the degree to which users enjoyed and liked the system as well as how content they were with using the
information presented (Dringus and Cohen, 2005, Zub and Eessaar, 2008).
At the fourth step for predicting LX scores of the questions Q1 to Q42 it was proposed to apply a ridge
regression model (Gilli and Schumann, 2010, Li, 2011, Schumann, 2012, Tharangie, 2008) allowing correlations
among checklist questions. The LX index LXI is calculated by aggregating the learner rate (effectiveness), time
(efficiency), and satisfaction components of learner experience. In order to accomplish this the learning rate is
assessed by a quiz. The learning time includes the time for online learning and the time for completing the quiz. The
learner satisfaction is measured by learner’s responses to the checklist questions.
Sensory design
Quiz
Q1:Q7
Interface design
Q8:Q10
Navigation design
Q11:Q15
Visual design
Q16:Q21
Learning design
Q22:Q27
Interaction design
Q28:Q32
Personalization design
Q33:Q34
Affective design
Q35:Q38
Reliability/maintainability
Q39:Q40
Satisfaction
Q41:Q42
Learning rate
Learning
time
Time
LX Index
LXI
Quiz time
Q43
Satisfaction
LX Index LXI calculation
Fig. 2 LX assessment model
In order to assess the LX-oriented assessment the entire LX construct is represented by a single dependent
variable LXI (cf. Fig. 2). The LX index LXI uses ten LX dimensions: sensory design, interface design, navigation
design, visual design, learning design, interaction design, personalization design, affective design,
reliability/maintainability and satisfaction. The most critical LX checklist items/dimensions are determined by the
coefficients of the ridge regression equation. Depending on the problems that these questions indicate, relevant
recommendations for LX design improvements of the eLearning system are then proposed.
The aggregated values of LX index are employed as a dependent variable of the ridge regression model. It
estimates the regression coefficients when the independent variables (checklist questions) are correlated amongst
themselves. This can lead to imprecise estimates of the regression model coefficients in case of using ordinary least
squared regression model. By low bias in the estimates, the ridge regression can reduce the variability of the
estimated coefficients and give a more stable and interpretable model.
p
The regression model is: Yi = β0 + ∑j=1 βj xij , where β0 , β1 , … , βp are the regression coefficients, the
number of independent variables (checklist questions) p = 42. 𝑥𝑖𝑗 denotes the answer to the jth question from the ith
learner and n is the number of learners.
Case Study
In order to illustrate and test the applicability of the framework proposed, a case study with a web-based
eLearning system on the topic of neo-tropical animal production was carried out. In the Department of Food
Production, University of the West Indies, under-graduate students of the Livestock Products Technology (AGLS
2004) and Ruminant Production Systems (AGLS 3003) courses were required to learn various aspects of neotropical wildlife utilization as part of their course syllabus. One topic common to both courses was “The Conversion
of Animals to Meat: The Harvesting of Muscle from Animals”. This was presented on the Open School of Tropical
Animal Science and Production eLearning system as Module 3. It detailed various bio-chemical changes that
occurred or will occur when muscle is converted to meat and the processes that must be followed before and after
slaughter for one non-domesticated neo-tropical species - the collared peccary alongside various domesticated
animals such as cows and chickens.
To test learner experience, 47 students were asked to complete a LX checklist after taking 15 minutes to
browse the website and read parts of Module 3. A further 20 minutes were allocated to completing a quiz
comprising 4 open-ended and 3 multiple choice questions. These were based on information in the aforementioned
parts of the module and related information on another portion of the eLearning website. Students were then asked
to complete framework checklist with 43 questions and 5 open ended questions to test 10 different LX dimensions.
Before the checklist was administered, a pilot test was conducted with a student who experienced problems in
navigation and learning design. The quiz and checklist were subject to slight modifications which enabled these
factors to be considered more in depth during the final eLearning system evaluation with undergraduate students.
The eLearning system assessment was administered in 3 batches on three days. Although this presents a
source of error as there were minor variations in time and location, these were necessary measures due to the
capacity of the computer laboratory, its availability and the turnout of students which were meager on some days.
The ridge regression model with sample size n=47 was used in this study. A regression model was
constructed relating RX index (LXI) to the other 42 variables – questions (Q). However, as expected, the 42 predictor
variables were themselves correlated. These imprecisions, caused by the multicollinearity amongst the predictor
variables, was what ridge regression was designed to overcome. The ridge regression equation was the following:
LXI = 2.50 + 0.018*Q1 + 0.074*Q2 - 0.004*Q3 - 0.158*Q4 - 0.098*Q5 + 0.205*Q6 + 0.101*Q7 + 0.198*Q8 +
0.067*Q9 - 0.073*Q10 - 0.297*Q11 - 0.217*Q12 - 0.213*Q13 + 0.123*Q14 + 0.140*Q15 + 0.096*Q16 +
0.229*Q17 - 0.068*Q18 - 0.041*Q19 - 0.042*Q20 + 0.014*Q21 - 0.025*Q22 - 0.125*Q23 + 0.266*Q24 +
0.204*Q25 + 0.084*Q26 + 0.042*Q27 - 0.192*Q28 + 0.118*Q29 + 0.068*Q30 - 0.113*Q31 + 0.083*Q32 +
0.022*Q33 - 0.099*Q34 - 0.262*Q35 + 0.079*Q36 + 0.201*Q37 - 0.123*Q38 + 0.323*Q39 - 0.166*Q40 0.212*Q41 + 0.060*Q42
The value of the ridge parameter was 0.02. The R-Squared (R2=71.6) statistic indicated that the model as
fitted explained 71.6% of the variability in LX I. The remaining 28.4% was attributable to deviations from the
regression model, which may be due to other factors, to learner estimation error, or to a failure of the current model
to fit the data adequately.
The most significant checklist questions for LX design of this eLearning system according to regression
coefficients in ridge regression equation were: 1) the website frequently shows new developments in neo-tropical
animal wildlife (Q39=0.32); 2) it was easy to understand course information in the module (Q24=0.27); 3) there are
no screens which appear to be too cluttered or too sparse (Q17=0.23); 4) the webpages fit my screen (Q6=0.20); 5)
information presented builds my knowledge on neo-tropical wildlife progressively (Q25=0.20); 6) the presentation
of information promotes my engagement with wild life learning screen (Q37=0.20).
The average LX index LXI was 2.61. Reduction of LXI would ultimately lead to a better learner experience.
This can be achieved by finding the checklist dimension with the higher scores. The learning design dimension had
the highest score of 0.86 and showed that improvements should be made on the course information and its
presentation. Feedback from 7 students showed that the separation of modules into different parts negatively
impacted their learning experience. Recommendations were to merge all parts of modules into one, enabling
knowledge building in a smooth, progressive manner, rather than having the hassle of opening multiple zip files to
continue readings on the same topic. In addition to this, 5 students complained that concepts and explanations in the
course manual were difficult to understand. Possible revision of lecture notes may be necessary to improve
explanations with case studies or examples.
The interface design dimension had the second highest score of 0.50 and this was most likely due to
difficulties students encountered when identifying information required for their learning needs. Three students
noted that links were labelled with module numbers alone without descriptions, and this wasted their time when
trying to access data needed to answer the quiz. To make the learning process more efficient, improvements should
be made by appropriately labelling links with both title and module number, as well as providing a preview of each
with a short description. By so doing, the number of errors made by choosing the wrong module would be reduced.
Following this, the reliability/maintainability dimension scored 0.41, showing that information required more
frequent updates on new developments in neo-tropical wildlife (Q17=0.23). It is therefore important to pay increased
attention to updating course manuals and, on the notice board section of the website, place recent reports and
publications on neo-tropical wildlife. One student highlighted this need and suggested a news bulletin. This can
prove to be of immense value to students considering the relatively small number of studies conducted on these
animals and the need to gather information and validate theories with external sources.
Further amendments to the visual design dimension, which scored 0.39, focusing on the display of
information on the screen (Q17=0.23) can lead to its improvement, alongside added videos and animations based on
various processes described in the text. From the open ended questions in the checklist, 22 students wanted to see
animations of topics such as the slaughter process, reproductive cycles and virtual farms, and 24 students were
interested in the site having videos to show processes such as the conversion of muscle to meat, milking, postmortem inspection and management practices. By so doing, the site can provide flexibility and hypermediality to
support the varying learning styles of users.
The sensory design dimension scored lower than visual design (0.14), however, it also spoke to the display of
information, in this case how webpages fit the screen (Q6=0.20). Three students found the site’s colour scheme dull
and 2 complained that the font size was too small. Recommendations are to redesign webpages so that they display
an appropriate amount of information in a font size which can be easily read by users on a variety of screen sizes.
Loading time for the site was also a complaint from 3 students and was largely due to the unfamiliar website
address. It was suggested that this website either be linked to UWI’s my eLearning site or the information
transferred to it. Although the latter could be possible, it is crucial to note that UWI’s my eLearning is a password
protected portal to information which is available only to enrolled UWI students. Transferring course information
here defeats the true purpose of the site which was to facilitate knowledge exchange with unrestricted access among
a broad spectrum of persons across the globe. In the same manner one student requested more frequent updates with
the current state of neo-tropical wildlife, which is often sourced from South America, this site should share and
encourage dissemination of its information to others.
Of particular importance was the navigation design dimension, as 5 students complained that navigation was
difficult and they encountered problems when searching for information required to complete the quiz. Two students
also made the suggestion that a search bar be incorporated into the site. Recommendations were to redesign it so that
specific information can easily be located, add breadcrumbs to help users with their orientation, and ensure the
consistent position of links throughout the website.
Conclusions
A framework for leaner experience-oriented assessing and design of eLearning systems on neo-tropical
animal production was developed. A case study with an eLearning system based on neo-tropical production
confirmed the applicability of the framework for supporting the LX design. Based on this study’s results, guidelines
and recommendations for LX redesign are defined. Students’ feedback primarily focused on difficulties experienced
accessing course information due to the division of modules into several parts and the compressed (zipped) folders
they were stored in. Thereafter, the lack of audio-visual material, followed by navigation was the most common
complaint. Corrective measures for redesign were proposed which encompassed improvements in the display,
navigation, relevancy and information access. An advantage of this approach may be the significant reduction of
time for redesigning of neo-tropical production eLearning systems and enabling students to better access
information relevant to their studies. It is hoped that when these corrective measures are implemented, the site can
become a more useful platform for frequent information dissemination and knowledge exchange on various species
of neo-tropical wildlife from persons across the world. By so doing, not only is research and information shared, but
conservation methods, which can help increase populations of these animals in both their natural habitat and in the
form of wildlife farming.
Further development and research can include combination of ridge regression model with genetic algorithm
(Ahn, 2012) or application of metaheuristic models like neural networks and swarm intelligence optimization. Also
usability testing can be performed to uncover design flaws, increase students’ retention and improve learning
experiences, engagements and outcomes (Dix, 2012, Monaco, 2012).
References
Ahn, J. J., Byun, H. W., Oh, K. J., et al. (2012). Using ridge regression with genetic algorithm to enhance real estate
appraisal forecasting. Expert Systems with Applications, 39 (9), 8369–8379.
Alexandre, G., Garcia, G. W., Rodriguez, L., et al. (2010). Tropical livestock farming systems, global framework
and case studies at the biotechnical and decisional levels. Advances in Animal Biosciences, 1 (2), 476-522.
Anderson, S. P. (2011). Seductive Interaction Design: Creating Playful, Fun, and Effective User Experiences.
Indianapolis, IN: New Riders.
Ardito, C., Costabile, M. F., Marsico, M. D., et al. (2006). An approach to usability evaluation of e-learning
applications. Universal Access in the Information Society, 4, 270-283.
Brown-Uddenberg, R. C., Garcia, G. W., Baptiste, Q. S., et al. (2004). The Agouti (Dasyprocta leporina, D. Aguti)
Booklet and Producers' Manual. Champs Fleurs, Trinidad: GWG Publications.
Chang, K. E., Sung, Y. T., & Hou, H. T. (2006). Web-based Tools for Designing and Developing Teaching
Materials for Integration of Information Technology into Instruction. Educational Technology & Society, 9 (4), 139149.
Chomba, S. & Olsen, C. S. (2008). E-learning in Tropical Forestry: Constraints and Options. In A.B. Temu, et al.
(Eds.), New Perspectives In Forestry Education, Nairobi, Kenya: ICRAF. 277-283.
Chua, B. B. & Dyson, L. E. (2004). Applying the ISO 9126 model to the evaluation of an e-learning system. In
Proceedings of the 21st ASCILITE Conference, Perth, Australia.
Constabile, M. F., Marsico, M. D., Lanzilotti, R., et al. (2005). On the usability evaluation of e-Learning
applications. In Proceedings of the 38th Hawaii International Conference on System Sciences, Hawaii, USA.
Constabile, M. F., Roselli, T., Lanzilotti, R., et al. (2007). A holistic approach to the evaluation of e-learning
systems. In Proceedings of the 4th international conference on Universal access in human-computer interaction:
applications and services, Beijing, China.
Dix, A., Roselli, T., & Sutinen, E. (2012). E-learning and human-computer interaction: exploring design synergies
for more effective learning experiences. In International Conference The Future of Education, Florence, Italy.
Dringus, L. P. & Cohen, M. S. (2005). An adaptable usability heuristic checklist for online courses. In Frontiers in
Education, Proceedings 35th Annual Conference, Fort Lauderdale, Florida.
Garcia, G. (2009). Neo-tropical Animals and Agro- tourism: A Trinidad and Tobago Opportunity. In Conference on
Turtle Conservation, Ecotourism and Sustainable Development, University of the West Indies, St. Augustine.
Garcia, G. W., Young, G. G., Amour, K. M., et al. (2005). Wildlife Farmers' and Producers' Booklet #2: The
Collared Peccary (Tayassu tajacu) Booklet and Producers' Manual. Champs Fleurs, Trinidad: GWG Publications.
Garrett, J. J. (2010). The Elements of User Experience: User-Centered Design for the Web and Beyond.
Indianapolis, IN: New Riders.
Gilli, M. & Schumann, E. (2010). Robust Regression with Optimization Heuristics. In A. Brabazon, M. O’Neill, &
D. Maringer (Eds.), Natural Computing in Computational Finance. NY: Springer.
Hartson, R. & Pyla, P. (2012). The UX Book: Process and Guidelines for Ensuring a Quality User Experience. MA:
Morgan Kaufmann.
Lanzilotti, R., Ardito, C., & Costabile, M. F. (2006). eLSE Methodology: a Systematic Approach to the e-Learning
Systems Evaluation. Educational Technology & Society, 9 (4), 42-53.
Li, S., Luo, A., & Zhuo, C. (2011). A Robust Relevance Vector Regression and Multi-swarm Cooperative Particle
Swarm Optimization Hybrid Algorithm for Coal Resources Evaluation. Journal of Information & Computational
Science, 8 (14), 3287-3294.
Monaco, E. J. (2012). Improving e-learning course design with usability testing. In International Conference: The
Future of Education, Florence, Italy.
Nielsen, J. (1993). Usability Engineering. CA: Academic Press.
Nogueira, S. S. C. & Nogueira-Filho, S. L. G. (2011). Wildlife farming: an alternative to unsustainable hunting and
deforestation in Neotropical forests? Biodiversity and Conservation, 20 (7), 1385-1397.
Ojasti, J. (1996). Wildlife utilization in Latin America: Current situation and prospects for sustainable management.
Rome, Italy: United Nations Food and Agriculture Organisation.
Peres, C. A. (2000). Effects of Subsistence Hunting on Vertebrate Community Structure in Amazonian Forests.
Conservation Biology, 14 (1), 240-253.
Peres, C. A. (2001). Synergistic effects of subsistence hunting and habitat fragmentation on Amazonian forest
vertebrates. Conservation Biology, 15 (6), 1490-1505.
Schumann, E. (2012). Robust regression with Particle Swarm Optimisation in R. Available at:
http://comisef.wikidot.com/tutorial:robustregressionwithheuristics. Accessed: 23 October 2012.
Tharangie, K. G. D., Irfan, C. M. A., Marasinghe, C. A., et al. (2008). Kansei engineering assessing system to
enhance the usability in E-learning web interfaces: colour basis. In 16th International Conference on Computers in
Education, Taipei, Taiwan.
The Open School of Tropical Animal Science and Production. (2012). Undergraduate Courses. Available at:
http://www12.brinkster.com/ostasp/courses1.aspx. Accessed: 20 October 2012.
Tullis, T. & Albert, W. (2008). Measuring the User Experience: Collecting, Analyzing, and Presenting Usability
Metrics. MA: Morgan Kaufmann.
Zarin, D. J., Kainer, K. A., Putz, F. E., et al. (2003). Integrated graduate education and research in neotropical
working forests. Journal of Forestry, 101 (6), 31-37.
Zub, D. & Eessaar, E. (2008). Pattern-based usability evaluation of e-learning systems. Innovative Techniques in
Instruction Technology, E-learning, E-assessment, and Education, 117-122.