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Measurement of learning motivation in electronic learning
Juliane C.a, Arman A.A.a, Sastramihardja H.S.a, Supriana I.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.The success of electronic learning (e-learning) process is highly determined by the extent of learning motivation that could be identified and measured through a number of methods. This study was designed to identify several attributes used to measure learning motivation in the context of e-learning as a chance of novelty in the domain of information extraction. The method by distributing questionnaires to 214 potential respondents with an interest in the field of study such as the e-learning facilitators including teachers, lecturers, instructors, laboratory assistants accustomed to assess or measure the participant’s learning motivation. The demography of respondents represents eight major islands in Indonesia, including Java, Sumatra, Kalimantan, Sulawesi, Papua, Bali, Nusa Tenggara, and Maluku with an expectation that the results of this study can represent a facilitator assessment towards e-learning Indonesia. A total of 1419 data has been collected and processed using Chi-Square method to test the hypotheses regarding the homogeneity of the respondents in choosing the candidates measurement of attribute in learning motivation. From the data processing, it can be concluded that the attributes of velocity, quantity, and relevancy can be further used as the variables in both the measurement of learning motivation in e-learning context and the field of information extraction.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]chi square,Electronic learning,Electronic learning (e-learning),Kalimantan,Learning motivation,Number of methods,quantity,relevancy[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]chi square,motivation measurement,quantity,relevancy,velocity[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICITSI.2015.7437732[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]