Zalba Zalba considers that a design and development of a Competency Based Education CBE is the best choice in order to set up a curriculum based on social context and necessities. Actually it is necessary to introduce the concept of academic competence or competence to generalize.
It is difficult to define objectively what an Academic Competence is, but it can be understood as a multidimensional construct composed of the skills, attitudes, and behaviors of a learner that contribute to academic success in the classroom. In general, a competence must entail a set of skills of problem solving — enabling the individual to resolve genuine problems or difficulties that he or she encounters and, when appropriate, to create an effective product — and must also entail the potential for finding or creating problems — and thereby laying the groundwork for the acquisition of new knowledge Gardner The CBE is an emerging curriculum model that tries to satisfy the demands of learning contexts, by the developing competencies, enabling students to act in a complex world in constant transformation Zalba In short, a competence is the ability to perform effectively in a given situation, it is based on knowledge but it is not limited to it Perrenoud In this new context, competences serve as well as unification and comparability tools.
Learners can develop a particular competence or set of competences in order to finally acquire certain knowledge, attitudes and professional capabilities. Tuning serves as a platform for developing reference points at subject area level. These are relevant for making programs of studies bachelor, master, etc. Reference points are expressed in terms of learning outcomes and competences. Learning outcomes are statements of what a learner is expected to know, understand and be able to demonstrate after completion of a learning experience.
According to Tuning, learning outcomes are expressed in terms of the level of competence to be obtained by the learner. In contemporary educational systems using CBE, flexible profiles are defined basing on the possibility of choosing with flexibility different subjects from a determined set. So, if students want to create their own academic profile, they only have to choose the subjects they consider interesting for their purposes.
But, in order to choose correctly, it is necessary to take into account that subjects are affected by competences in two ways:. A subject needs that students accomplish certain level of development for specific competences before students are ready to confront this subject.
Each subject itself contributes to the development of certain competences in a major or minor degree. So, in order to acquire certain development of competences, learners must study subjects in a retroactive way, so that stepwise they develop the desired competences. In other words, to course certain subjects it is necessary to have studied and passed those subjects capable of develop competences prerequisite of more advanced subjects. We will take these ideas as basis for our further explanation of the use of CB in Academic Orientation.
In Castellano b it was presented OrieB a DSS based on collaborative filtering which offers recommendations to students as subjects susceptible of being good elections in order to perform an adequate academic path. As collaborative techniques deal with customers, items and ratings, OrieB deals with students, subjects and marks , so the dataset about Academic Orientation is adapted to apply CF techniques. Another point is that we considered generically that students with similar marks share similar skills.
So if we analyze the performance of students in a given group, G i , in different curriculum modalities. This analysis might be then utilized to support future students classified in G i for their academic decisions so that OrieB consists of:. Grouping students: students are grouped based on the similarity of its marks and those from other students. This similarity is computed used Pearson Correlation Coefficient Castellano b.
Predictions: system makes a prediction about the mark that student would obtain for candidate subjects so this prediction could be used by the advisors in order to support student decisions. Data analysis about the performance of CF in academic orientation and hence in OrieB was done and presented in Castellano b.
The results the survey showed that the system was capable to support successfully advisors in an accurate way. A further detailed explanation about the previous survey, the decision support model and OrieB can be revised in Castellano b. This approach has certain limitations suffered by Orieb which we are trying to solve in this chapter:.
New item problem : whenever institutions due to either new laws or academic needs a new subject is required, OrieB has no possibility of recommend it as it has no marks available from students, because no one has study this subject yet and no one has a mark, and CF is unable to works without this kind of data.
So far we have seen the use of CF in academic orientation and some limitations that it presented such the cold start. Consequently a good way to overcome the cold start might be the use of the hybridizing with content based techniques as it has been done in other applications. In this section we propose a content based model for academic orientation that afterwards it will be hybridized with the CF technique already revised.
These profiles need to be based on a set of keywords or features. In our case, items will be subjects and users will be students. Features chosen to elaborate profiles are Academic Competences. Following European Union guidelines, Spanish Academic System is based in eight competences which are:.
First of all, we need to explain how will be designed and formed the subject and student profiles. A subject needs that a student had developed certain set of competences if he or she wants to pass it.
So, the subject profile will consist in the competences and de level of development that the subject needs to be studied correctly. For example, the subject Spanish Literature will need a high level of development for Linguistic and Arts competences and hardly in Mathematics competence. So, we need to know which level of development each subject needs for each competence.
This information was gathered by means of a questionnaire. We ask a big number of teachers from several High Schools in which degree in a scale they think that subjects they teach need from students to have been developed each competence.
In order to be consequent with subject profiles, student profiles must be built based on competences. In a specific moment each student has a degree of development for each competence. For example, it is known that some students outstand in Mathematics and Digital competences, but lack of Initiative and Arts ones, while on other students can happen just the opposite, or any other combination.
Spanish Academic System is about to evaluate by means of competences so that in a nearby future will be possible to know the level of development of each competence in any moment. However, in the present time this is not completely real.
Once we have got these results, we grouped subjects by grades in order to aggregate with a weighted average the percentage in which each subject contributes with each competence. Supposing that in a certain grade we have only three subjects, an example of the result of our quiz can be seen in Table 5. The student profile can be seen on Table 7. Now, with subjects and students profiles, we will see how a CBRS would work in order to make a recommendation.
To provide recommendations, system needs to find those subjects that require a competence degree such that user had already developed. This is not exactly to find subjects with profiles equal to a student profile, but to find subjects profiles with required level of competences equal or lower that level in user profile. For example, looking at Table 4 we could think that a student with 5 in all competences will not be recommended any subject as he or she do not match well with the levels specified.
However, the reality is that this student is able of studying any of them, because the requirements are fulfill enough. To solve this question, we need a similarity measure which treat as well student which exceed requirements as students that simple fulfill them. For this reason we will not use similarity measure explained before, the cosine, and we will use a normalized variant of the Euclidean distance upgraded in order to follow this guideline, hence if student has a greater level for the desired competences, the similarity will give a positive result.
This is achieved by using the minimum between the level required for the subject and the level accomplished by the student, instead of using only the level accomplished.
This way the system treats equally those subjects with overpassed and simply fulfilled competence requirements, giving priority to those competence requirements not accomplished. Consequently, let r i,c be the required level of development for competence i in subject c, and v i,s the computed value for student s in competence i.
In our case, interval of values used is between 1 and 5, both included. The greater the value obtained the greater prepared will be the student to course that subject.
This equation will be applied to those subjects belonging to the target grade which student has to study next. The most often used technique for the generation of the top-N list is the one that select the N most valuated candidates with the similarity measure. In section 4 was overviewed OrieB Castellano b , a web based Decision Support System built to support Spanish advisors in their task of helping students which modality to choose in Baccalaureate, after finishing Secondary School. Specifically, the system aided advisors to obtain useful information about which subjects in each modality and which elective subjects suited better a student or which core subject might be extra hard.
Thanks to this system advisors can develop their duties quicker and with reliable information. However as it was pointed out, this system presented overall the new item problem which makes impossible to offer complete recommendations because in a continuous changing system, new subjects appears almost every year and CF is no able to support this kind of information. To solve this limitation and those seen in previous sections a new Hybrid-OrieB system has been built, using both CF and CB approaches in order to provide better recommendations and to overcome CF inherent limitations.
Home Page of OrieB. Due to the importance that the information provided by this system can perform in the future decisions of students in early ages that they are not mature, we decided that it will be used just for advisors in order to support students but not directly by the students due to their lack of maturity.
CF is unable to recommend this kind of subjects. This fact points out to the use of CB, as CB will always provide a recommendation for every target subject so that new subjects will not be a problem. At this point, the system would obtain 2 lists of recommendations, one from CB and one from CF. Provided that CF uses 15 top subjects to elaborate its recommendation, we will set N for CB also in So, we will have 2 lists of 15 subjects each with which we are going to work the hybridization.
If dataset has no marks for a selected subject, CB recommendation will be used and the subject will be used to build the recommendation, because it is a new subject. If dataset presents any marks for that subject, system will weight recommendation of CF and CB using its own computed CF trust Castellano a , Castellano b.
Let scf be the similarity computed by means of CF, scb the similarity computed by means of CB, and t trust computed for CF recommendation.
New utility function for this subject will be computed as follows:. If CF selects a subject not included by CB, it also will be used to perform the final recommendation because CF can offer serendipitous recommendations. With this list system only has to order by similarity and recommend the same as done in the old OrieB. Once presented how system works internally in this section we will show what kind of recommendations and information OrieB offers and how to obtain it.
The latter choice Figure 4 offers the possibility of entering several marks instead of using all of them in order to obtain a more general orientation. This way so the more marks filled the more accurate and customized will be the advices obtained by the system. Manual filling of marks. Module or Vocational Program Recommendations. In order to aid advisors guiding students about the Module that better suits them according to their marks OrieB computes a Module recommendation based on a ordered list by interest see Figure 5.
Interest value expresses the appropriateness of a module for the target student based on the predicted marks. System offers a linguistic term to make and explain recommendations than precise numerical values that can mislead the students in their decisions.
So, OrieB will provide linguistic recommendations Castellano a. Trust value shows the confidence about the previous interest value based on the ratio between the number of subjects whose predictions were obtained and the total number of subjects for the module, and the standard deviation of those predictions Castellano a. Vocational Program Recommendation. Support for choosing Elective and Module Subjects. Once students have chosen what module they prefer, they need to complete their curriculum with module and elective subjects.
To support this decision OrieB offers separate recommendations for each group of subjects see Figure 6. Subject recommendation in OrieB. Warning Difficulties in Core Subjects.
Finally, students also may need advices about what core subjects could be difficult for them. In this sense, the system offers a list with those core subjects with predictions lower than medium, it will warn the advisor which core subjects could cause difficulties to the student, with a trust level of the recommendation.
Core subject difficulty advising. In this chapter we have introduced the problem of Academic Orientation, presented a system which make use of Collaborative Filtering techniques in order to recommend students an academic path by using their marks, and studying its limitations, proposed a hybrid CB and CF system in order to overcome the problems of scarcity and new item problem.
This system helps advisors in their task of supporting students and opens a matter of study in the Academic Orientation with CBE in which academic profiles are going to be more flexible and systems more capable of giving better recommendations for students in matter of improve and develop capabilities. The origin of upgrading the support model for academic orientation with content based techniques is because of the recent adaptation of the Spanish Academic System to the Competence Based Education.
This change provoked the appearance of new subjects and profiles that CF models in OrieB cannot managed because of the lack of data. Consequently to overcome the cold start problem with these new subjects and due to the available information the more suitable model to achieve the goal of supporting advisors was the hybridizing with a content based model which provides a higher coverage but regarding the accuracy we need to wait to obtain real data sets.
Additionally this upgraded version is ready to be extended and useful amid the ongoing changes of the academic system. Eventually, we want to highlight though OrieB is performing pretty well, there exist different challenges that should guide our research in the future:.
OrieB does not take into account subjective information provided by students such as preferences, yet. So the system should be able to include not only information relative to their academic tour but also subjective and own information.
Information provided by the system should not directly guide students because some reasoning about the results are necessary, so only advisors can use OrieB. More visual and self-explicative recommendations would be needed in this sense not only for allowing students using the system but also for providing advisors a better way of exploring and explaining academics alternatives to students. So far OrieB is focused on secondary and Baccaulerate grades.
It seems interesting its extension to higher education. Licensee IntechOpen. Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. Edited by Chiang Jao. We are IntechOpen, the world's leading publisher of Open Access books.
Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Castellano, Manuel J. Downloaded: Introduction It is common that in all academic systems the students must make decisions about the future by choosing among different alternatives that include professional profiles or modalities , elective subjects or optional , etc.
Collaborative recommender systems Collaborative recommender systems CRS collect human opinions of items, represented as ratings, in a given domain and group customers with similar needs, preferences, tastes, etc.
According to Figure 2 , both models fulfill three general tasks to elaborate the recommendations demanded by users: Analyzing and selecting data sets : data from ratings must be collected and optimized for the system Herlocker Collaborative filtering methods provide several advantages regarding other techniques used in recommender systems Herlocker , Sarwar : Capability to manage information whose content is not easily analyzed automatically, because they do not need knowledge domain, i.
Ability to filter items based on quality and taste, not only on its features. Content-based recommender systems Content-Based Recommender Systems CBRS recommend similar items to those ones that the user liked in the past by comparing various candidate items with items previously rated by the user and the bestmatching item s are recommended Adomavicius , Jung , Symeonidis Table 1.
Item description. Hybrid recommender systems Due to limitations observed in both previous recommendation techniques, nowadays it has been used to overcome these limitations the hybridization technique. Following it is presented the classification of hybridizing techniques for recommender systems presented by Burke Burke a : Weighted systems: recommendations of each system are combined giving each one a specific weight of the final recommendation, depending on the system which computed them.
Educational systems The concept of academic orientation is related to the student curriculum guidance, it means that students have to make decisions about their curriculum in order to obtain a degree in the topic they prefers the most or their skills are the most appropriate. Evaluation The main point that all academic institutions and educational systems have in common, is that they evaluate their students by means of different evaluation tools tests, essays, tasks, exercises, etc.
Table 3. Specialization Most educational systems all over the world from early educational stages to University degrees allow students to choose among different specialization branches according to their skills, preferences, attitudes and marks, building a personalized so-called Academic Profile.
Academic orientation tasks. Advisors Students must make hard decisions about the future since early ages despite their personality and maturity could not be enough to make properly those important decisions. Regarding Academic Orientation, the advisors usually face two main types of students.
Competency based education In a changing world based in changing technology and services, it is necessary for academic systems to have solid foundations capable of allowing a flexible configuration of learning process formulated as flexible academic profiles.
But, in order to choose correctly, it is necessary to take into account that subjects are affected by competences in two ways: A subject needs that students accomplish certain level of development for specific competences before students are ready to confront this subject. Profile construction First of all, we need to explain how will be designed and formed the subject and student profiles. Subject profile construction A subject needs that a student had developed certain set of competences if he or she wants to pass it.
Table 4. Subject profiles constructed from competences. Student profile construction In order to be consequent with subject profiles, student profiles must be built based on competences. This may be that participants in Group A were Haaga-Helia students, who were already familiar with the facilities on the Pasila campus.
Therefore, Group A provided more negative feedback regarding the user flow and navigation within the application Table 4. Group B, however, gave more positive, open feedback regarding their user experience, the concept design itself, and the VR concept of the Pasila campus Table 5. However, the participants in Group B were not familiar with the information or content of the application and had trouble learning from the user interface and using the application intuitively. Therefore, they struggled with the navigation as well as the amount of information in their memory load.
Participants in Group A rated the application by grouping the functions together, and then they determined if the overall tasks were completed Table 4. This created a polar state between participants whose ratings were all high and those whose ratings were all low.
On the contrary, participants in Group B gave the application a neutral assessment, because they had not experienced the Haaga-Helia system previously Table 5. In summary, the usability evaluation showed that both first time and experienced users were able to use the application. Moreover, the existing functions were useful and only needed to be improved for performance and user experience enhancement.
In addition, the application received positive ratings from Group A and positive open feedback from Group B. Therefore, the research team continues to work toward resolving the existing problems to complete every aspect of the application. This study demonstrated the concept of interactive and augmented m-learning for Orientation Week at an institution of higher education.
Past research has emphasized the importance of student engagement in learning and teaching Vuori, , where technology-enabled learning methods provide solutions to enrich situational learning opportunities. Additionally, the concept presented in this study is instructive regarding the integration of technology-enabled learning methods into other conventional orientation activities, such as tutoring and team building. This is in line with the research on blended learning that highlights the effectiveness of creating more meaningful learning experiences through integrating interactive and augmented m-learning into activities that take place on the campus premises see, e.
New students are often young and have never studied at a higher educational institute before, or been away from their home country. Social pressure from home can lead to emotional conflict, such as fear of failure, and moving alone to an unfamiliar place can cause loneliness. Different factors in the environment can lead to anxiety, stress, and pressure, which reduce concentration.
Hence, Orientation Week is often not as efficient as it can be. Nonetheless, the Orientation Week does provide a wealth of information to students about the environment and their learning context. It also provides an introduction to the study path, which helps students make decisions about their future career. Nevertheless, not all information is visible during Orientation Week, and the fact that it is mandatory can be stressful for new students.
Thus, institutions of higher education should provide an alternative solution to the traditional Orientation Week that provides a fun and relaxing experience to freshmen during their first engagement with university life. If the solution can offer students the safe, open, stimulating environment of the real university premises before they arrive at school, their first impression will be more positive, and they can gain more confidence by getting used to the virtual space.
Moreover, the evaluation results of the application prototype indicated that the students felt positive and impressed with the concept design and that it would serve their purposes efficiently. Giving students the power to decide when and where they are studying could empower them to continue with their studies.
Without the external pressure of mandatory orientation days and with the continuous availability of information, students can move forward at their own pace and customize their learning schedule to suit their preferences. The technologies to implement in the final product are the AR and VR functionalities of the application regarding the virtual tour of the designated campus and within the Moodle learning environment.
Students are able to access the dynamic and interactive study content via their phone camera or their own VR headset. As the number of students who own a smartphone, laptop, or tablet grows every year, it is necessary to create an m- learning environment that is mobile. The most familiar solution for Haaga-Helia students is to upgrade the existing online learning environment to an engaging and interactive solution for their personal mobile devices.
The usability testing and evaluation process were conducted with 12 participants, divided into Groups A and B. The solution provided by the team is a mobile orientation application for freshmen at universities and polytechnics. More specifically, to exhibit the concept, Haaga-Helia and orientation to the BITe program were chosen. The solution provides a way for new students to gain all the relevant information that a week-long orientation contains at their own pace, through their mobile phone.
This solves the problems of missing days of orientation due to illness or late flights, and it reduces the number of man-hours needed on the part of the school to organize the orientation. The remaining challenges to be tackled before the concept can be implemented are to refine the features, the user interface design, as well as the architecture design and implementation.
Furthermore, the team will continue to develop the concept design into a completed, functional, hi-fi prototype. Discussions with the participants revealed that students would not find the application interesting if it were just an informational website or a list of instructional videos. To implement the solution, the team will adopt Unity, as it has good support for creating AR and VR applications.
The team is expected to have a working application to test on BITe students in fall Based on the feedback given by new students, the application will be developed further and potentially adapted to other degree programs at Haaga-Helia. The future of a learning environment such as this concept design can be developed to fit the growth of modern technology when VR headsets become more common and affordable for everyone. The learning environment can be completely virtualized with real-time interaction and socialization, similar to the current VR Chat application.
Moreover, the AR aspect can be enhanced so that virtual lectures and practical workshops can be shown via HoloLens. The pursuit of higher education is becoming global, where individuals from around the world can attend classes across continents and at different universities.
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Nguyen, N. An interactive and augmented learning concept for orientation week in higher education. Download citation. Received : 14 April Accepted : 02 July Published : 08 October Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Abstract This paper details the concept development process for an interactive and augmented reality based application that compensates for attending Orientation Week at a higher education institution.
Background It is conventional for higher education institutions to organize an Orientation Week for new students. Research questions and design methodology The aim of this study to come up with an application concept that helps freshmen to learn about their educational institutes.
Hence, this study pursues to answer the following question: What kind of mobile application may assist freshmen to adjust their new educational environment? Full size image. Application concept design User study and data collection Various user study methods were deployed to gather information on how students carried out the tasks, such as questionnaires and semi-structured interviews.
Table 2 User-task matrix during Orientation Week Full size table. Table 3 List of tasks recognized during the data analysis Full size table. Sample of application concept as a scenario.
Screenshots of the application concept. Usability test evaluation scenario. Table 4 Post-test heuristic evaluation: Group A Full size table. Table 5 Post-test heuristic evaluation: Group B Full size table.
Discussion This study demonstrated the concept of interactive and augmented m-learning for Orientation Week at an institution of higher education. References Abras, C. Google Scholar Acevedo, J.
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