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Masterarbeiten
(How) Can collaborative MOOCS be analyzed? - Development of criteria for the selection of suitable analysis/analytics tools
Art der Abschlussarbeit
Bachelor
Autoren
- Johansson, Per Philip Leonard
Betreuer
- Prof. Dr. rer. pol. habil. Eric Schoop
- Dipl.-Wirt.-Inf. Michel Rietze
Abstract
Abstract
cMOOCs: a new E-Learning tool dating back to the year 2012, offering new approaches to
how we learn in today’s technology oriented world. The concept is based on collaborative
learning, sharing and repurposing information amongst participants, in order to achieve
personal and educational goals at a self-set pace.
The vast number of available providers and platforms, and the structural component of
courses taking part online, allows for mass data collection of user behavior, spanning over
hundreds of thousands of data entries. But the question of how to utilize the collected data still
remains. In fact, the analyzability cMOOCs as a whole has yet to be explored.
This paper focuses on the collaborative Massive Open Online Course, giving an introduction
into its general structure and background based on literature from known sources such as
Dillenbourg, Carr and Rodriguez. Different viewpoints for analysis are deducted in order to
gain an overview of the different analytical comparative factors available and to show the
possibilities as well as the need for analysis. Using these criteria, the main focus is placed on
the data analytical aspect of cMOOCs in order to extract the possibilities for analysis of data
collected throughout sample courses. Finally, a criteria matrix for the selection of appropriate
data analytical tools and instruments is created, allowing for the selection of an analysis-focus
and scope dependent tool, giving the possibility for further analysis on specific tools
themselves, and/or detailed, provider/course specific data analysis.
Next to selection criteria, the paper demonstrates the importance for cMOOC analysis with
regard to comparative factors such as course quality, allowing direct comparison to be made
between different courses based on these factors. Additionally, findings show that given the
cMOOCs young age, there still remain several undeveloped aspects of the cMOOC relating to
questions regarding accreditation and quality assessment, showing a need for a type of
standardization in order to handle these discrepancies.
Key Words: cMOOCs, Collaborative Learning, Analytics Tools
cMOOCs: a new E-Learning tool dating back to the year 2012, offering new approaches to
how we learn in today’s technology oriented world. The concept is based on collaborative
learning, sharing and repurposing information amongst participants, in order to achieve
personal and educational goals at a self-set pace.
The vast number of available providers and platforms, and the structural component of
courses taking part online, allows for mass data collection of user behavior, spanning over
hundreds of thousands of data entries. But the question of how to utilize the collected data still
remains. In fact, the analyzability cMOOCs as a whole has yet to be explored.
This paper focuses on the collaborative Massive Open Online Course, giving an introduction
into its general structure and background based on literature from known sources such as
Dillenbourg, Carr and Rodriguez. Different viewpoints for analysis are deducted in order to
gain an overview of the different analytical comparative factors available and to show the
possibilities as well as the need for analysis. Using these criteria, the main focus is placed on
the data analytical aspect of cMOOCs in order to extract the possibilities for analysis of data
collected throughout sample courses. Finally, a criteria matrix for the selection of appropriate
data analytical tools and instruments is created, allowing for the selection of an analysis-focus
and scope dependent tool, giving the possibility for further analysis on specific tools
themselves, and/or detailed, provider/course specific data analysis.
Next to selection criteria, the paper demonstrates the importance for cMOOC analysis with
regard to comparative factors such as course quality, allowing direct comparison to be made
between different courses based on these factors. Additionally, findings show that given the
cMOOCs young age, there still remain several undeveloped aspects of the cMOOC relating to
questions regarding accreditation and quality assessment, showing a need for a type of
standardization in order to handle these discrepancies.
Key Words: cMOOCs, Collaborative Learning, Analytics Tools
Schlagwörter
MOOCS, Analytics Tools, cMOOCs, Collaborative Learning, Analytics Tools
Berichtsjahr
2014
Diplomarbeiten
(How) Can collaborative MOOCS be analyzed? - Development of criteria for the selection of suitable analysis/analytics tools
Art der Abschlussarbeit
Bachelor
Autoren
- Johansson, Per Philip Leonard
Betreuer
- Prof. Dr. rer. pol. habil. Eric Schoop
- Dipl.-Wirt.-Inf. Michel Rietze
Abstract
Abstract
cMOOCs: a new E-Learning tool dating back to the year 2012, offering new approaches to
how we learn in today’s technology oriented world. The concept is based on collaborative
learning, sharing and repurposing information amongst participants, in order to achieve
personal and educational goals at a self-set pace.
The vast number of available providers and platforms, and the structural component of
courses taking part online, allows for mass data collection of user behavior, spanning over
hundreds of thousands of data entries. But the question of how to utilize the collected data still
remains. In fact, the analyzability cMOOCs as a whole has yet to be explored.
This paper focuses on the collaborative Massive Open Online Course, giving an introduction
into its general structure and background based on literature from known sources such as
Dillenbourg, Carr and Rodriguez. Different viewpoints for analysis are deducted in order to
gain an overview of the different analytical comparative factors available and to show the
possibilities as well as the need for analysis. Using these criteria, the main focus is placed on
the data analytical aspect of cMOOCs in order to extract the possibilities for analysis of data
collected throughout sample courses. Finally, a criteria matrix for the selection of appropriate
data analytical tools and instruments is created, allowing for the selection of an analysis-focus
and scope dependent tool, giving the possibility for further analysis on specific tools
themselves, and/or detailed, provider/course specific data analysis.
Next to selection criteria, the paper demonstrates the importance for cMOOC analysis with
regard to comparative factors such as course quality, allowing direct comparison to be made
between different courses based on these factors. Additionally, findings show that given the
cMOOCs young age, there still remain several undeveloped aspects of the cMOOC relating to
questions regarding accreditation and quality assessment, showing a need for a type of
standardization in order to handle these discrepancies.
Key Words: cMOOCs, Collaborative Learning, Analytics Tools
cMOOCs: a new E-Learning tool dating back to the year 2012, offering new approaches to
how we learn in today’s technology oriented world. The concept is based on collaborative
learning, sharing and repurposing information amongst participants, in order to achieve
personal and educational goals at a self-set pace.
The vast number of available providers and platforms, and the structural component of
courses taking part online, allows for mass data collection of user behavior, spanning over
hundreds of thousands of data entries. But the question of how to utilize the collected data still
remains. In fact, the analyzability cMOOCs as a whole has yet to be explored.
This paper focuses on the collaborative Massive Open Online Course, giving an introduction
into its general structure and background based on literature from known sources such as
Dillenbourg, Carr and Rodriguez. Different viewpoints for analysis are deducted in order to
gain an overview of the different analytical comparative factors available and to show the
possibilities as well as the need for analysis. Using these criteria, the main focus is placed on
the data analytical aspect of cMOOCs in order to extract the possibilities for analysis of data
collected throughout sample courses. Finally, a criteria matrix for the selection of appropriate
data analytical tools and instruments is created, allowing for the selection of an analysis-focus
and scope dependent tool, giving the possibility for further analysis on specific tools
themselves, and/or detailed, provider/course specific data analysis.
Next to selection criteria, the paper demonstrates the importance for cMOOC analysis with
regard to comparative factors such as course quality, allowing direct comparison to be made
between different courses based on these factors. Additionally, findings show that given the
cMOOCs young age, there still remain several undeveloped aspects of the cMOOC relating to
questions regarding accreditation and quality assessment, showing a need for a type of
standardization in order to handle these discrepancies.
Key Words: cMOOCs, Collaborative Learning, Analytics Tools
Schlagwörter
MOOCS, Analytics Tools, cMOOCs, Collaborative Learning, Analytics Tools
Berichtsjahr
2014
Bachelorarbeiten
(How) Can collaborative MOOCS be analyzed? - Development of criteria for the selection of suitable analysis/analytics tools
Art der Abschlussarbeit
Bachelor
Autoren
- Johansson, Per Philip Leonard
Betreuer
- Prof. Dr. rer. pol. habil. Eric Schoop
- Dipl.-Wirt.-Inf. Michel Rietze
Abstract
Abstract
cMOOCs: a new E-Learning tool dating back to the year 2012, offering new approaches to
how we learn in today’s technology oriented world. The concept is based on collaborative
learning, sharing and repurposing information amongst participants, in order to achieve
personal and educational goals at a self-set pace.
The vast number of available providers and platforms, and the structural component of
courses taking part online, allows for mass data collection of user behavior, spanning over
hundreds of thousands of data entries. But the question of how to utilize the collected data still
remains. In fact, the analyzability cMOOCs as a whole has yet to be explored.
This paper focuses on the collaborative Massive Open Online Course, giving an introduction
into its general structure and background based on literature from known sources such as
Dillenbourg, Carr and Rodriguez. Different viewpoints for analysis are deducted in order to
gain an overview of the different analytical comparative factors available and to show the
possibilities as well as the need for analysis. Using these criteria, the main focus is placed on
the data analytical aspect of cMOOCs in order to extract the possibilities for analysis of data
collected throughout sample courses. Finally, a criteria matrix for the selection of appropriate
data analytical tools and instruments is created, allowing for the selection of an analysis-focus
and scope dependent tool, giving the possibility for further analysis on specific tools
themselves, and/or detailed, provider/course specific data analysis.
Next to selection criteria, the paper demonstrates the importance for cMOOC analysis with
regard to comparative factors such as course quality, allowing direct comparison to be made
between different courses based on these factors. Additionally, findings show that given the
cMOOCs young age, there still remain several undeveloped aspects of the cMOOC relating to
questions regarding accreditation and quality assessment, showing a need for a type of
standardization in order to handle these discrepancies.
Key Words: cMOOCs, Collaborative Learning, Analytics Tools
cMOOCs: a new E-Learning tool dating back to the year 2012, offering new approaches to
how we learn in today’s technology oriented world. The concept is based on collaborative
learning, sharing and repurposing information amongst participants, in order to achieve
personal and educational goals at a self-set pace.
The vast number of available providers and platforms, and the structural component of
courses taking part online, allows for mass data collection of user behavior, spanning over
hundreds of thousands of data entries. But the question of how to utilize the collected data still
remains. In fact, the analyzability cMOOCs as a whole has yet to be explored.
This paper focuses on the collaborative Massive Open Online Course, giving an introduction
into its general structure and background based on literature from known sources such as
Dillenbourg, Carr and Rodriguez. Different viewpoints for analysis are deducted in order to
gain an overview of the different analytical comparative factors available and to show the
possibilities as well as the need for analysis. Using these criteria, the main focus is placed on
the data analytical aspect of cMOOCs in order to extract the possibilities for analysis of data
collected throughout sample courses. Finally, a criteria matrix for the selection of appropriate
data analytical tools and instruments is created, allowing for the selection of an analysis-focus
and scope dependent tool, giving the possibility for further analysis on specific tools
themselves, and/or detailed, provider/course specific data analysis.
Next to selection criteria, the paper demonstrates the importance for cMOOC analysis with
regard to comparative factors such as course quality, allowing direct comparison to be made
between different courses based on these factors. Additionally, findings show that given the
cMOOCs young age, there still remain several undeveloped aspects of the cMOOC relating to
questions regarding accreditation and quality assessment, showing a need for a type of
standardization in order to handle these discrepancies.
Key Words: cMOOCs, Collaborative Learning, Analytics Tools
Schlagwörter
MOOCS, Analytics Tools, cMOOCs, Collaborative Learning, Analytics Tools
Berichtsjahr
2014