1. how mobile devices used for improving access to learning resources and evaluation system (i.e., enabling students to look at information any time and anywhere);
2. new changes in teaching and learning processes (i.e., revision material tailored to the needs of the individual, providing a flexible context-awareness system that can react to their needs); and
3. the establishment of relationships between academic goals and business (i.e., allowing users to use their personal devices with educational purposes, blending mobile technologies into e-learning infrastructures to improve interactivity and connectivity to the learner).
1. the common characteristics of the whole set of activities, such as available languages, general set description, adaptation features to be considered, and general aspects related to collaboration workgroups,
2. information for each activity (type, descriptions, deadline, and so on),
3. different versions of contents associated to each activity, and
4. collaborative tools to be offered to support collaborative task accomplishment. This information is stored in the activity model.
• Relationships between activities, which can be established in different ways according to the type of user(s) for which they are intended.
• Navigational guidance offered within each set of activities, which can also be different according to user needs or preferences (direct guidance versus free).
• (Un)suitability of certain types of activities according to the type of activity and the user(s) context. This can also be established in different ways for different types of users, even in the same context.
• Specific activity accomplishment requirements: If they are not satisfied, the activity is not proposed to the corresponding users. Different requirements can be specified even for the same activity, each of them for a certain type of users.
• Collaborative workspace configuration: Problem statements and collaborative tools can be differently combined to generate different collaborative workspaces, even for tackling the same task, each of them adapted to each group of users' features and needs, stored in the group model [ 19].
• Recommended. Prerequisites, if any, are satisfied, and the activity is suitable for the user context.
• Not recommended. Prerequisites, if any, are satisfied, but the context of the user is not appropriate to perform the activity.
• Available. There is no pending prerequisite for the activity to be performed, although there is no information about the suitability of the activity for the user context.
• Not available. Any condition related to user personal features or previous actions is not satisfied.
• Already done. The user can access to it again.
• Green: recommended at that time.
• Yellow: available but not recommended in this context.
• Red: not available.
• Orange: available.
• Black: already accomplished.
• A link of recommended activities from the recommendation area.
• Any link to recommended, not recommended, or already performed activities in the activity index (area ). Not recommended activities are annotated as not suitable in the index (yellow color), but the learning environment does not block the access to them. Unavailable activities are annotated in red color and students cannot access to them until the recommendation state changes.
• The button "next recommended activity" included at the bottom of area . In this case, the workspace that will be presented will correspond to the first activity in the list of recommended activities.
• Creating a set of activities (whose realization and recommendation will be supported), and defining types of activities and common characteristics of the whole set.
• Providing general context filters related to the (un)suitability of certain types of activities in different contexts.
• Specifying the sets of tools to be used to accomplish collaborative activities.
• Describing the learning activities themselves, along with structural rules and accomplishment requirements (if any), and specifying the different versions of contents associated to each activity.
• Personal features: Learning styles (visual-verbal, active-reflective, and sensing-intuitive dimensions), as defined in Felder's model .
• Actions previously done: Activities already accomplished and results in practical tasks.
• Context: Device used (PC, laptop, or PDA), available time (numerical value), and physical location (classroom, laboratory, home, or others).
• It is better with recommendations.
• It does not matter.
• It is better without recommendations.
• These systems guide one over the whole set of activities and help to decide the starting point (what are the best activities to be done according to one's personal needs and learning process).
• It helps to know which topics have been wrongly learned, and it proposes review activities for consolidating these concepts.
• It includes many exercises and I can train for the final exam since teachers do only a few exercises in class.
• It is useful that the system annotates the most important topics of the whole set of topics.
• These environments are more attractive because they allow me to do many types of activities, not only study theory from a book or my personal notes.
• This type of learning environments helps to organize one's free time, so they are very useful when one has only a few minutes available.
• E. Martin is with the Escuela Politécnica Superior, B-207, Universidad Autónoma de Madrid, c/ Francisco Tomás y Valiente 11, 28049 Madrid, Spain. E-mail: email@example.com.
• R.M. Carro is with the Escuela Politécnica Superior, B-318, Universidad Autónoma de Madrid, c/ Francisco Tomás y Valiente 11, 28049 Madrid, Spain. E-mail: firstname.lastname@example.org.
Manuscript received 18 Sept. 2008; revised 20 Nov. 2008; accepted 20 Dec. 2008; published online 31 Dec. 2008.
For information on obtaining reprints of this article, please send e-mail to: email@example.com, and reference IEEECS Log Number TLTSI-2008-09-0090.
Digital Object Identifier no. 10.1109/TLT.2008.24.
Estefanía Martín received the PhD degree in 2008 from the Universidad Autónoma de Madrid and is a teaching assistant at the Universidad Rey Juan Carlos. She has been working in adaptive hypermedia and collaborative learning since 2002. Her PhD thesis dealt with the recommendation of activities in mobile environments and dynamic workspace generation, taking into account user personal features, actions, and context. Her research focuses on adaptive hypermedia, collaborative systems, and mobile learning. More information can be found at http://www.eps.uam.es/~emartin.
Rosa M. Carro received the PhD degree in 2001 from the Universidad Autonoma de Madrid and is an associate professor at the same university. Her research focuses on adaptive hypermedia, user modeling, mobile learning, and collaborative systems. She did research on adaptive educational games at the University of Aveiro in 2001-2002. She joined the Cooperative Systems group of the Technical University of Munich in 2002. She is a member of several associations and committees, such as the IASTED Committee on Education, AIPO, and ADIE. She has coorganized international workshops such as Adaptive Hypermedia and Collaborative Systems at ICWE 2004 and the series of workshops on Authoring of Adaptive and Adaptable Hypermedia at AIED 2005, AH 2006, UM 2007, and AH 2008. She is a reviewer for international journals such as IEEE Internet Computing and the IEEE Transactions on Knowledge and Data Engineering. She is a coauthor of more than 70 publications related to her research areas.