Music students’ behavior on using learning objects closer to the domain characteristics and the social reality
Introduction
Human capital refers to the knowledge, information, ideas, skills, and health of individuals. The global economy cannot succeed without considerable investment in human capital (Becker, 2002). Modern economies require that people continuously invest in the acquisition of knowledge, skills, and information.
Livingstone (1997) affirms the core thesis of human capital theory is that peoples’ learning capacities are comparable to other natural resources involved in the production process; when the resource is effectively exploited the results are profitable both for the enterprise and for society as a whole. Therefore, in the knowledge intensive society, learning plays an important factor.
Learning requires contextual knowledge, i.e., still in recent years, increased and detailed attention has turned toward the question of domain specificity. Humans simply could not come to know what they do know in a purely domain-neutral fashion (Hirschfeld & Gelman, 1994). On the other side, teaching is also domain specific (Gess-Newsome & Lederman, 2001). Therefore, the specificities of the domain must be considered in education and content closer to the domain characteristics can improve learning.
Other important aspect to learning is technology support. The use of personal computers, mobile technology and internet has changed the way people think, communicate and live (Wei & Young, 2011). These changes can also be seen in the Educational area, where advanced technology aims at supporting and improving the way people learn. Computer supported distance learning environments (also called as on-line distance learning or simply e-learning) started to emerge and were rapidly spread (Rózewski, Kusztina, Tadeusiewicz, & Zaikin, 2011), in such a way that the development of e-learning solutions was considered one of education areas that had grown faster at the ending of the 20th century (Gilbert, Morton, & Rowley, 2007). This model is strongly used both on school and corporate education.
Similarly to these changes on the technologies used for teaching, it’s also important to highlight the emergence of a new society, constituted of individuals who have born and grew together with the expansion of this everyday technology use. Called as Generation Y the ones who were born after the 1980s and Generation Z who were born after the 1990s (Grail Research, 2013), these people understand the use of technology on their activities better and more natural than prior generations. These new human beings, also called as Digital Natives (Prensky, 2001), use technology for leisure, to work and also in education.
The use of online social networks platforms is growing, especially by Digital Native students. For instance, a study (Smith & Borreson Caruso, 2010) conducted with 30.616 undergraduate American students showed that 90.3% of them dedicate time every day to use social networks’ environments. The education literature background also states that social interactions are important to the learning process (for instance on Vygotsky, 1986). Thus, the use of online social networks environments seems appropriate to be exploited on education scenario to improve apprentices’ learning performance.
In addition, it is important to analyze how students use the resources offered in an education activity in order to extract their behavioral pattern. Through this understanding, a lot of information can be derived, such as those to customize the student experience with Learning Objects (LO) or to cluster similar students together (Wolpers, Najjar, Verbert, & Duval, 2007), and metrics to find out what and how certain resources influence the acceptation of the used technologies by students (Pynoo et al., 2011).
This paper presents a model for developing learning objects (LO) that are closer to the domain characteristics and in accordance to the learners’ social reality. In order to demonstrate the applicability of the model, we applied it to the context of music education. Then, we developed sound manipulation resources (taking into account the domain characteristics) as well as social resources (considering the social reality). After that we performed a case study using these resources in a learning activity and analyzed the students’ behavior.
The main objective of our research was to explore the students’ behavior on using LO that were built according to a model that ensures the interconnection of social and domain adapted elements. Will the resulting LO assist the students to learn the required concepts? Which characteristic will be more demanded by the student? What are the consequences on student’s behavior on using the technology?
This paper is organized in 5 more sections. Section 2 presents characteristics associated to the student’s behavior on using learning technologies, mainly those related to the acceptance of technologies and more specifically the usage intention. Section 3 explains the importance of the Music domain as well as the C(L)A(S)P Model, whose applicability we also reinforce through a generalization of its concepts and related theories. Section 4 describes our approach for developing Learning Objects and Activities based on the C(L)A(S)P Model. Some resources that were developed with this approach and an example of a LO are also described. Section 5 describes the case study and the results. Finally, in Section 6 we present some final remarks.
Section snippets
How to analyze the students’ behavior?
In order to analyze the students’ behavior on using learning objects that are closer to the domain characteristics and to the social reality, we considered concepts related to the acceptance of technologies, such as Learning Opportunities, Social Influence, Ease of Use and Hedonic Motivation, as well as Facilitating Conditions and Usage Intention. These concepts are related to the Technology Acceptance Model (TAM) (Davis, 1989), Unified Theory of Acceptance and Use of Technology (UTAUT) (
The music domain and the C(L)A(S)P Model
Although human capital is usually related to functional knowledge and skills, and strongly connected to the enterprises areas, culture may also represent an important driver of economic growth. Bucci and Segre (2011) analyzed the existence of a relationship of complementarity between cultural and human capital investments. According to their research, in the long run a higher growth rate of real per-capita income can be attained the more cultural and human capital investments are complementary
Developing learning objects/activities based on the C(L)A(S)P Model
In order to support the development of learning objects and activities that are closer to the (music) domain and considering social resources, basic components (such as action logs, object transmission, and object sharing) are combined or specialized according to the domain (such as sound manipulation log, sound transmission, and sound sharing on Facebook). After that, these components are classified according to the C(L)A(S)P parameters and stored as C(L)A(S)P resources (for instance, pitch
Case study
The ten developed LO were used in a case study with twenty-three (23) high school students in a basic music education class. The participants’ average age was about 15 years old.
The unit of analysis was the individual performance of the students’ musical education activity using the developed LO and the comment resource. The objective of the main activity was to explore the concept of the sound pitch parameter. The learning objective was to understand that the higher the pitch, the more acute is
Conclusions
The proposed model provides different perspectives for the development of human capital so that it is possible to foster innovation (through the Creation parameter), critical thinking (Analysis parameter), better production (Practice), theoretical grounding (Literature) and skill and abilities (Skill) in a collaborative environment (Interaction).
A model like LO-C(L)A(S)PI that integrates Creation, Analysis, Practice, Literature, Skill and Interaction features (or Composition, Audition,
Acknowledgments
We acknowledge the high school which allowed us to validate this research and also to every student who participate on the case study. A special thanks to the teacher who believed on our ideas and supported us to make this work possible. This work was partially supported by FAPERJ (through grants E-26/170028/2008 INC&T Program – Project: Brazilian Institute of Research on Web Science, and E-26/ 101.509/2010 – BBP/Bursary Representation and contextualized retrieval of learning content), and CNPQ
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