Online Educa Berlin' eLene EE Economics of elearning

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Online Educa Berlin. eLene- EE (Economics of
e-learning)
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Index

• Welcome (Mikael Sjöberg) 5 minutes
• Project overview and connection with main
  objectives (David Castillo) 5 minutes
• WP1 Cost-Benefit Analysis (Niklas Hanes and
  David Castillo) 30 minutes
• WP2 Students Achievement (David Castillo and
  Toni Femenias) 30 minutes
• Coffee break 20 minutes
• WP3 E-learning Indicators (Susanna Sancassani
  and Andrzej Wodecki) 40 minutes
• WP4 E-learning and Digital Divide (Adel Ben
  Youssef) 40 minutes
• Summary and topics for further discussion
  (Deborah Arnold) 10 minutes

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Project overview Economic Framework

• Rapid knowledge creation and easy access to
  knowledge the emergence of a knowledge-based
  economy
• ICT can be seen as a suitable technological base
  for knowledge economy development
• The main hypothesis is that ICT are the technical
  paradigm on which current dynamics of the
  industrial revolution is based.
• ICT can be situated at the material basis of the
  economic growth for many developed countries
  since 1995.
• Productivity increase is consistent with a
  positive trend in labour quality explained by the
  rise in average levels of educational attainment.
• It is confirmed the existence of
  complementarities between technical and
  organisational change and skilled labour input
  through the demand for specific skills and
  abilities.

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Project overview Economic Framework

• Universities have important challenges
  generalise access to education, improve
  educational attainment levels, respond to social
  demand of lifelong learning and fit workers needs
  of specific skills and abilities.
• E-learning is a good opportunity for universities
  to reach these objectives, as a general diffusion
  of education may lead to significant benefits.
• Individual benefits higher productivity and
  wages, higher likelihood to participate in the
  labour market, greater probability to experience
  less unemployment, effects on health, on
  intergenerational cognitive development, better
  analytical skills, better adoption of consumption
  technology, higher saving rates.
• Social benefits improvement of productivity
  levels and rates of economic growth and spillover
  effects to the whole society.
• eLene-EE objectives
• WP1. Efficiency
• WP2. Students performance
• WP3. Indicators
• WP4. Digital divide.

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WP2- Student performance of e-learning

• The diffusion of ICT infrastructure in higher
  education tools has induced important changes,
  not just on the pedagogic sphere, but also
  related to administrative and organizational
  issues.
• The increasing use of the online learning tools
  and its diversity allow students have more
  choices in an online course than they used to
  have in a traditional face-to-face environment.
• Two main questions
• Does the use of ICT affect student performance?
• Does the use if ICT affect student performance
  differently depending on the subject?

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WP2- Analysis of student performance through
production functions

• Which variables affect students achievement?
• The analysis of student performance will allow us
  testing the relations between achievement,
  earnings, institutional variables (organisation,
  methodology, technology) and students profile.
• Some analysis constrains the multidimensional
  nature of educational outputs, the lack of market
  value measures for some of the educational
  process results and the joint production of these
  different educational outputs (Maddala, 1977).
• Two alternative approaches to specify the
  relation between educational inputs and outputs
• The production function
• The frontier production functions

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WP2- Theoretical models

• The technical relation that underlies education
  production functions can be expressed as follows
  (Hanushek, 1986)
• 
  
• Where
• A represents the achievement of a student I at
  period t.
• Xi is a vector of ability, attitudes and
  socio-demographic characteristics for student I
  at period t.
• H is a vector of inputs for university I at
  period t. Within this group we should include
  four different set of variables
• Institutional variables, related to the level of
  institutional commitment towards ICT adoption.
• Technological variables, linked to the use of
  different ICT devices for teaching and learning
  purposes.
• Methodological variables.
• Teachers inputs, related to the degree of
  technology and methodology uses by teachers.

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WP2- Empirical models

• The most simple and common functional form to
  describe the technical relation between inputs
  and outputs is Cobb-Douglas function, which can
  be expressed as follows
• 
  or
• Cobb-Douglas function has an important
  constraint, i.e. the fact that substitution
  elasticity between inputs is equal to one.
• Two alternative functional forms
• The CES production function
• It can be estimated through the use of different
  methods, for instance Kmenta method (1962),
  transformation method by Box and Cox (1964) or
  procedures to adjust non-lineal models (Zellner,
  1971).
• The translog production function
• It can be estimated by conventional econometric
  methods

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WP2- Data needed and survey design

• We need to collect information from students who
  attend different courses or modules where some
  use ICT while others dont.
• The variables collected trough the survey can be
  gathered into some general categories student
  preparation, student and family characteristics,
  students ability, how students used the course
  materials, and the characteristics of educational
  institutions.
• If we want to compare the results between the
  online and the face-to-face methods will be
  suitable that the survey is responded in the same
  period.
• The socioeconomic characteristics of the country
  or region must be included by the investigator,
  in order to obtain the peculiarities and
  similarities of and between regions.
• To evaluate the influence of the diversity of
  learning tools, the questionnaire also must be
  focused on how the students used the course
  materials.

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WP2- Data needed and survey design

• We must send out a questionnaire to students in
  order to collect the following information
• Grade (fail, pass, pass with distinction, or
  something else). This will be our dependent
  variable, y .
• Sex
• Age
• College grades
• Type of college exam (science, social science,
  practical)
• Numbers of semesters at university level
• Students attitude (endogenous)
• Time use (endogenous)
• Other activities (work, club activities, see
  Löfgren, 1998)
• We will also need the following information to
  control for other potentially important
  determinants of student performance
• Restricted intake (admission)..
• Collaboration. To what extend are collaboration
  part of the teaching process.
• Class size.
• Type of exam (written test, exam paper etc.).
• Teacher (name, sex, education).

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WP2- Hypothesis and expected results

• There is a consensus that an appropriate use of
  digital technologies in higher education can have
  significant positive effects both on students
  attitude and achievement (Talley, 2005).
• Empirical results show a worse performance of
  online students respect to their face-to-face
  counterparts (Coates et al., 2004 or Brown and
  Liedholm, 2002). However, these results are not
  related with students characteristics.
• Brown and Liedholm (2002) conducted an empirical
  study where can be observed that students who are
  enrolled in an online course have better
  characteristics than the live students.
• 
  

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WP2- Hypothesis and expected results

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WP2- Hypothesis and expected results

• Are significant these differences?
• Brown and Liedholm (2002) conclude that the
  difference between performances of the two
  methods is significant.
• Coates et al (2004), although its results
  indicate that students in face-to-face courses
  use to score better than their online
  counterparts, argue that this difference was no
  significant. This difference is due to the
  importance of the self-selection into online
  courses and its effects on the determination of
  students outcomes.
• Students characteristics like ability or prior
  experience affect in his/her performance
• The better results in the exams that live
  students show can be due, at least in part, to
  differences in the student effort. Student
  effort, expressed in hours allocated to study,
  tend to be higher among live students than online
  students

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WP2- Hypothesis and expected results

• The fact that universities supply digital devices
  does not necessarily mean that these tools are
  used, since often educators are precisely the
  ones that remain reluctant to its utilization in
  their subjects.
• One of the possible causes of this reluctance is
  the fact that the introduction of ICT-based tools
  in teaching methods require more time for
  teachers than with traditional methods ( Becker
  and Watts,2001).
• The benefits of technology may not be uniform
  across the student characteristics (ability,
  gender, or prior experience)
• Brown and Liedholm (student preferences in Using
  Online Learning Resources) use the concept of
  cognitive styles to explore the role of
  differences in student abilities, past learning
  in the subject, attitudes, and aptitudes make in
  the explanation of learning achievements.
• These authors argue that a students having a
  cognitive style is analogous to the students
  having a production function for learning, and
  indeed, the cognitive style determines the
  underlying shape of the learning curves or the
  students production function for learning.

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WP2- Hypothesis and expected results

• Among the diversity of materials available in the
  course the students will value better those who
  consider concordant with their diverse cognitive
  styles.
• To contradict the belief that those instructors
  that use technologies in their classes spend more
  time that those who dont make use of them.
• The instructor who use technology with high
  intensity spend the same amount of time in their
  teaching activity that those who are more
  reticent to use technology tools in their
  classes. ( Sosin et al, 2004).
• No longer concern because the real significant
  issue is in what manner technology is used at
  university, teachers and students level (Sosin
  et al., 2004)

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WP2- Hypothesis and expected results

• Table 3- Fixed- Effect Panel Regression with
  Institution Cross Group

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WP2- Methodological constraints

• Econometric models of the production function of
  education may have some estimation problems
  related to endogeneity, data censoring,
  measurement and self-selection (Becker 2001
  Becker and Powers, 2001 Sosin et al. 2004).
• The data-censoring problem arises if the
  dependent variable has an upper or lower bound
  that limits the measurement of the student
  performance.
• OLS (ordinary least- squares) regression
  specification is the most common econometric
  model used to measure the differential impact of
  online courses on educational outcome.
• Some inconveniences of this model
• Sosin, K. et al (2004) point out that
  econometric models of the production of learning
  may have estimation problems related to
  measurement, self-selection data censoring and
  endogeneity
• Coates et al. (2004) argue that a potential
  shortcoming of the OLS regression procedure is
  that it is possible that an individuals choice
  between distance learning and face-to-face
  instruction is affected by unobservable
  differences in ability and learning styles. In
  this case, OLS estimates of the parameters would
  be biased and inconsistent due to endogeneity.

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WP2- Methodological constraints

• If the decision of the mode of instruction
  selection is related to each students expected
  performance under each method of instruction, OLS
  is not an appropriate specification.
• There are alternative econometric models
• The 2SLS ( two stages least-squares)
  specification.
• Switching equations models with endogenous
  switching
• Maximize or minimize the educational production
  function?
• The majority of the authors tend to maximize the
  educational production function. It means that
  ICT tools have been created to maximize the gains
  available to students.
• However other authors, like Talley (2005) hold
  that students will seek the amount of learning
  that they believe appropriate to earn a desired
  grade at the minimum cost possible. As Talley
  conclude they may be considered cost-minimizers
  when it comes to learning.