Pa ge 1 Pa ge 13 8 American Journal of Multidisciplinary Research and Innovation (AJMRI) Technology Acceptance among College Students Living in Remote Areas Queen Karezza L. Albofera1, Dayanara A. Digan1, Jenny Rose Torres1, Rodeth Jane C. Quezada2 Volume 3 Issue 4, Year 2024 ISSN: 2158-8155 (Online), 2832-4854 (Print) DOI: https://doi.org/10.54536/ajmri.v3i4.2898 https://journals.e-palli.com/home/index.php/ajmri Article Information ABSTRACT Received: June 20, 2024 Accepted: July 27, 2024 Published: July 31, 2024 The extent to which students adopt technology in their learning process has long been the focus of research. This study aimed to determine the level of technology acceptance and the differences in the level of learning style analyzed by sex, age, marital status, and year level. The participants of this study are 205 college students officially enrolled in the School Year 2021- 2022. This study used the adopted survey questions by Davis (1989). Frequency, mean, and Pearson-r were used as statistical treatment of data. Results showed that the overall level of technology acceptance of the respondents was high that the level of technology acceptance among college students, as grouped by sex and across ages, shows no significant difference. However, in terms of year level and program, third-year college students got the highest level of technological acceptance. Among all the indicators, Perceived ease-of-use (PEU) obtained the lowest mean score. With this, the researchers recommend conducting a seminar entitled “Blended Learning: The Emerging Technologies” for students to be able to know the significance of technology and to continue embracing technological advancement, especially during this time of the pandemic. Keywords Perceived Usefulness (PU), Perceived Ease-of-Use (PEU), User Satisfaction (US), Attribute of Usability (AU) INTRODUCTION The advancement of internet technology and new methods of sharing information has contributed to the emergence of various online learning scenarios. However, it is much harder to design an online environment for fundamental work in technology fields where students have to do hands-on exercises and work in labs as part of a course learning process (Estriegana & Barchino, 2021). The apparent COVID-19 pandemic and government ban have forced some countries to switch from face-to- face classes to online models to enable higher education for students. The factors influencing students’ use and acceptance of emergency online learning are diverse and have been investigated using technology use and acceptance models. However, this cross-sectional study presents current emergencies, and students investigate the factors specific to the use and acceptance of emergency online learning (Hermida & Garza, 2021). The technology acceptance model (TAM) was designed based on the literature review; this model aims to evaluate and investigate the usability test for Perceived Usefulness, Perceived Ease of Use, User Satisfaction, and Attribute of usability. TAM adapts the Theory of Reasoned Action (TRA) to the field. It posits that perceived Usefulness and ease of use determine an individual’s intention to use a system to mediate actual system use. Perceived Usefulness is also seen as being directly impacted by perceived ease of use (Gefen & Larsen, 2017). Over the past two decades, technology-enhanced character education has been widely promoted and used sparingly (Deifell & Angus, 2021). Due to the COVID-19 pandemic, all educational institutions worldwide have been closed, and education delivery has now shifted to an “online-only” monopoly model. In this regard, the perceived usability of current e-learning platforms is essential, especially in the absence of physical classrooms (Pal & Vanija, 2020). Aburagaga and Agoyoyi (2020) recently introduced teaching technology to include online, remote, and flexible learning for some institutions to respond strategically to the growing demand for access to higher education. The significant investment in online education platforms, technologies, and infrastructure facilities makes it difficult for many developing countries to implement online learning approaches. Collectively called Emergency Distance Education (ERE), a temporary change in the delivery of guidance caused by the outbreak of a crisis. Establish g ERE does not leave traditional placement of the educational process or a creation of an entirely new educational system. It provides a temporary viable alternative for educators to conduct classes and provide students with the necessary classroom support (Hodges et al., 2020). In the Philippines, distance learning shows the digital divide among Filipino students (Santos, 2020). This current situation in distance education is likely exacerbating existing inequality; it created barriers to online learning. Data gathered in a national cross-sectional study resulted in twenty-two percent (22%) and thirty-two percent (32%) of 3,670 Filipino medical students surveyed having difficulty adapting to new learning styles and the lack of reliable internet access (Baticulon et al., 2020). For some students, purchasing a learning device that makes it easy to log into online courses and return homework instantly to the online system can be challenging. Despite efforts to make education accessible to all, many challenges face Philippine university students in distance learning Mateo (2020). 1 Faculty of the Department of Teacher Education UM Digos College, Digos City, Philippines 2 University Of Mindanao Digos College, Philippines * Corresponding author’s e-mail: karezzaalbofera@gmail.com Pa ge 13 9 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 138-147, 2024 Technology acceptance model (TAM) is an information system theory that describes how consumers come to accept the use of technology, according to Davis’s (1989) idea of TAM. According to the model, a number of factors influence users’ decisions about when and how to employ new technology when it is provided to them. User satisfaction, perceived usefulness, perceived ease of use, and usability attribute are these criteria. Furthermore, Emaeilzadeh (2016) asserts that users encompass roles other than that of technology users, such as citizens and customers. Therefore, the distinct qualities of IT users influence their adoption behavior, and the TAM model is not always the best model to describe user acceptance. According to Lim (2018), there are situations where the Technology Acceptance Model (TAM) is too general and trivializes social and self-regulatory components of user behavior, changes in the socioeconomic environment, and the proliferation of technology. As a result, the TAM is not useful. Therefore, in order to examine user acceptability and the proliferation of mobile banking, Khan and Mir (2016) focused on user acceptance utilizing the diffusion innovation theory framework, TAM drivers, and internal and external factors. This study was conducted over Davao Del Sur, Davao Occidental, via a Google form. The respondents of this study are college students living in remote areas. This study aimed to determine if they accept the technology in their areas despite having no internet connections due to limited access to internet connectivity in their location. Hence, according to the result, the data revealed that college students living in remote areas, even though they are no internet connection, can use their mobile phones by going to rural areas to have access to the signal or the internet connection. Therefore, school administrators may find the study’s conclusions useful as a reference when evaluating their learning environment and how they support students’ and teachers’ usage of technology in mobile learning. Additionally, it can help teachers focus on the outcomes of their students, such their academic achievements. With the aid of technology, a teacher could help a pupil develop a more optimistic view and successful learning process. Lastly, I am able to assist not just upcoming researchers but also stakeholders in education at all levels. Future scholars, educators, and students/pupils should therefore be aware of the acceptability of and strategies for using technology effectively. Research Objectives This study aimed to determine technology acceptance among college students living in remote areas. It sought to answer the following: 1. To identify the profile of the respondents in terms of: 1.1 Sex; 1.2 Age; 1.3 Marital status; and 1.4-year level. 2. To determine the level of technology acceptance among college students in terms of: 2.1 Perceived Usefulness (PU); 2.2 Perceived Ease-of-Use (PEU); 2.3 User Satisfaction (US); and 2.4 Attribute of Usability (AU). 3. To determine if there is a significant difference in the technology acceptance among college students living in a remote area when grouped according to profile. METHODOLOGY Respondents The target respondents of this study were college student’s from 1st Year to 4th Year level enrolled in the School Year of 2021 – 2022. Using stratified random sampling, the respondents were randomly selected from various programs, year level, age, and marital status. The distribution was based on the proportion of students enrolled in the specified school year. The respondents included in this survey are only those students who consent to participate in the data gathering. In choosing the research participant’s of this study, the following inclusion criteria were strictly followed: (1) the students are officially enrolled in the UM Digos College for the AY 2021 – 2022, (2) students come from any department of UM Digos College, (3) students either; first, second, third, or fourth-year students living in remote areas. Moreover, the withdrawal criteria are the following; (1) unwillingness to participate in the study, (2) unstable internet connection, (3) if the respondents answer no from the Google form that the researchers sent. Instruments The instruments were composed of two parts. Part 1 dealt with the demographic profile of the respondents in terms of their sex, age, marital status, and year level. Part 2 dealt with technology acceptance among college students, which has four indicators; perceived Usefulness (PU), perceived ease-of-use (PEU), user satisfaction (US), and attribute of usability (AU). The second part was a 22-item questionnaire from Technology Acceptance Model (TAM) by Davis (1989) and modified by Nair and Das (2011). Hence, the researchers did not conduct the reliability test. In this study, the researchers used a 5-Likert scale to interpret the students’ responses to the level of technology acceptance. The scale below was used to analyze the data. Table Scale Range of Means Descriptive Levels Interpretation 5 4.20 – 5.00 Very High The respondents’ level of technology acceptance is always practiced. Pa ge 14 0 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 138-147, 2024 Design and Procedure In this study, the descriptive-comparative design was used. As explained by Calderon (2006, as cited by Rillo & Alieto, 2018), descriptive research is collecting, examining, categorizing, and organizing statistics regarding existing situations, practices, procedures, trends, and cause- effect associations. It also made appropriate and precise explanations of such information with, without, or sometimes with minimal statistical procedures. To determine the sources of technology acceptance among college students, the researchers strictly observed and followed these steps to gather data. First, the researchers wrote a letter for authorization from the Vice- President Branch Operations of UM Digos to allow the researcher to conduct the student in the school. Second, upon approval, the researchers create a Google Form for them to start a survey about the study. Third, after making the said Google Form, the link has been created, and they send it to their random respondents to answer the survey. Fourth, after answering those respondents, the researchers collect the result and talled them. Lastly, after the results were talled, the researchers sent them to their statistician. After tabulation, the data were analyzed and interpreted using the mean, frequency, independent sample, t-test, and ANOVA Lariosa, et al., (2023). RESULTS AND DISCUSSION Profile of the Respondents Table 1 shows the distribution of the participants in the study in terms of sex, age, marital status, and year level. A total of 205 respondents voluntarily expressed their intention to participate in the study. Sex The table shows that out of 205 or 100% of respondents of the study, 54 are male, approximately 26.3%, and 151 are female, which is approximately 73.7% of the total respondents. Therefore, this study implies that females dominate the respondents, and Males got the lowest frequency in the study. Age The table revealed that ages 17-20 years old got a frequency of 27, which is approximately 13.2%; ages 21-23 years old got a frequency of 159, which is approximately 77.6%; and 24 years old and above got a frequency of 19 approximately 9.3%. Therefore, this implies that in terms of the age range of the respondents are dominated by 21-23 years old got the highest frequency while 17-20 years old and 24 years old above got the lowest frequency. Marital Status The table reveals that out of 205 respondents, 197 belong to single status, 7 are happily married, while the remaining 1 is widowed. Therefore, the results revealed in terms of marital status is dominated by single, with approximately 96.1% of the overall study. Year Level The table shows the respondents in terms of the year level; in the first year, there are 7, which is approximately 3.4%, second year, with a frequency of 6, which is approximately 2.9%; in the third year, there are 167 which is approximately 79.0%, and in the fourth year there are 30 which approximately 14.6%. Therefore, in terms of the year level, the results revealed that the third-year level got the highest frequency while the second- year, first- year, and fourth-year got the lowest frequency. Students’ Level of Technology Acceptance Table 2 presents the level of technology acceptance among college students in terms of Perceived Usefulness (PU) got a mean score of 4.29, Perceived Ease-of-Use (PEU) with a mean score of 4.11, User Satisfaction (US) got a mean score of 4.18 and Attribute of Usability (AU) got a mean score of 4.14. The overall mean score of the four indicators is 4.18 (SD=0.369). Therefore, the four indicators are being measured by the student’s level in technology acceptance. 4 3.40 – 4.19 High The respondents’ level of technology acceptance is oftentimes practiced. 3 2.60 – 3.39 Moderate The respondents’ level of technology acceptance is sometimes practiced. 2 1.80 – 2.59 Low The respondents’ level of technology acceptance is seldom practiced. 1 1.00 – 1.79 Very Low The respondents’ level of technology acceptance is never practiced. Table 1: Characteristics of 205 students included in the study Profile f % Sex Male 54 26.3 Female 151 73.7 Age 17-20 years old 27 13.2 21-23 years old 159 77.6 24 and above 19 9.3 Marital Status Single 197 96.1 Married 7 3.4 Widowed 1 .5 Year Level First Year 7 3.4 Second Year 6 2.9 Third Year 162 79.0 Fourth Year 30 14.6 Pa ge 14 1 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 138-147, 2024 Perceived Usefulness (PU) As shown in Table 2, the level of technology acceptance in terms of Perceived Usefulness (PU) obtained a mean score of 4.29 (SD=0.555). It signifies that the respondent’s level of technology acceptance are very high. Perceived Usefulness is “the degree to which a person believes that using a particular system will increase his or her job performance” (Davis, 1989). In previous studies, perceived usefulness is a strong and direct determinant of continuance usage intentions (Mouakket, 2015; Wu & Chen, 2017). It also has a positive influence on users’ satisfaction. Such association is found in different studies like e-learning systems, Electronic textbooks, mobile banking (Yuan et al., 2016), and mobile commerce (Luqman et al., 2016). For instance, Alrajawy et al. (2017) explored the facets that impact the intention to use and found that perceived Usefulness significantly differed in the intention to use online learning in Yemen. Perceived Ease-of-Use (PEU) As shown in the Table 2, the Students’ level of technology acceptance of respondents in terms of Perceived Ease- of- Use (PEU) obtained a mean of 4.11 (SD=0.467) as agree. It signifies that the respondents’ level of technology acceptance was high. Moreover, it is believed that the extent to which an individual perceives that utilizing a particular system will be effortless. Shahrabi et al. (2016). PEU increases the likelihood that a user will approve a technology if they believe it to be more convenient to use than another, according to a study by Moslehpour et al. (2018). Thus, the more straightforward a technical. The more appealing an application is, the more probable it is that users will use it (Moslehpour et al., 2018). The degree of experience a user has greatly influences how user-friendly a system is. According to Caffaro et al. (2018), people who are new to or inexperienced with new technology are more likely to experience issues engaging with it and to report it. Consequently, the effect that experience may have on the homogeneity of the sample population is taken into account in an efficient research design. User Satisfaction (US) As shown in Table 2, the level of technology acceptance of the respondents in terms of User Satisfaction (US) obtained a mean score of 4.14 (SD=0.398). It signifies s that the respondents’ level of technology acceptance was high. Moreover, it is believed that users get satisfied with using technology daily as a mode of communication, business transactions, and even online classes. In ECM, one of the key ideas is satisfaction. In the research (Ouyang et al., 2017). Users’ decision to continue using IS is influenced by their perception of usefulness and contentment with prior experiences, according to the ECM. Previous research has confirmed the association between consumers’ satisfaction and their intention to continue using a service (Lee, 2010; Alraimi K. et al., 2016). Attribute of Usability (AU) As shown in Table 2, the level of technology acceptance of the respondents in terms of Attribute of Usability (AU) obtained a mean of 4.14 (SD=0.422). It signifies s that the respondent’s level of technology acceptance was high. Moreover, it is believed that how students use technology significantly impacts their behavioral use of technology. Dobozy and Reynolds (2016) assert that one of the key features of an LMS that draws users in is its usability. A key factor in the success of classroom technology is the attitudes of students regarding its use. Sheingold and Hadley (1990) conducted research on how educators used computer software in the classroom. Word processing software, educational software, analytical and informational tools, operating systems and programming, games and simulations, graphics and operating tools, and word processing software were among the technologies examined. It was shown that, if teachers are given the time and support to become knowledgeable about the technology, their attitudes toward computers and instructional software can have a considerable impact on their pupils’ attitudes toward the same. Even after many years, the attitudes of instructors continue to be crucial for integrating technology into the classroom (Demetriadis, et al., 2016). Table 2: Students’ level of technology acceptance, n = 205 Indicators ̅𝒙 SD Perceived Usefulness (PU) 4.29 0.555 Perceived Ease-of-Use (PEU) 4.11 0.467 User Satisfaction (US) 4.18 0.398 Attribute of Usability (AU) 4.14 0.422 Overall 4.18 0.369 The perceived ease of use of a technological system and its presentation is related to its accessibility. Users’ perceived ease of use is one of the most important criteria in their acceptance of a technology, according to Davis’s (1986) Technology Acceptance Model (TAM). According to Davis (1989), ease of use refers to how much users believe a system will save them time and effort. Stated differently, users are more likely to use a system if they perceive it to be user-friendly. The fundamental presumptions of the Technology Acceptance Model (TAM) state that a person’s acceptance of technology acts as a mediator in how they utilize it (Jones & Kauppi, 2018). The e-Filing system’s performance risk is influenced by perceived simplicity of use. Performance hazards will be reduced with a simpler e-Filing system. Perceived simplicity of use has an impact on the intention to utilize the system, according to certain studies (Novindra & Rasmini, 2017; Riyadh et al., 2016; Zaidi et al., 2017). PEU is a crucial component in analyzing and evaluating user acceptability of innovative technologies, claim Pa ge 14 2 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 138-147, 2024 Significant Difference in the Level of Technology Acceptance among College Students as Analyzed by Sex Table 3 shows the independent samples t-test comparing the differences in the responses in terms of sex. The result shows that there are no significant differences between male (mean = 4.19, SD = 0.408) and female (mean = 4.18, SD = 0.355) students’ level of technology acceptance, t(205)= .212, p = 833. This indicates that both sexes have almost similar levels of technological acceptance. Furthermore, when it came to students’ learning satisfaction with regard to accepting technology, there were no appreciable gender disparities (Harvey et al., 2017). Popovich et al. (2008, quoted by Othman & Al Othman, 2016) looked at college students’ opinions about technology. Table 3: Independent samples t-test results showing the differences on students’ level of technology acceptance when analyzed by sex Indicators Group n ̅𝒙 SD t p Perceived Usefulness (PU) Male 54 4.24 0.552 -.823 .412 Female 151 4.31 0.557 Perceived Ease-of-Use (PEU) Male 54 4.12 0.470 .182 .856 Female 151 4.10 0.468 User Satisfaction (US) Male 54 4.23 0.486 .977 .332 Female 151 4.16 0.362 Attribute of Usability (AU) Male 54 4.17 0.450 .565 .573 Female 151 4.13 0.413 Overall Male 54 4.19 0.408 .212 .833 Female 151 4.18 0.355 *p<0.05 They discovered the opinions of those men and women regarding technology. Gender differences in technological adoption characteristics were found to be minimal or nonexistent in other studies (Teo et al., 2015 & Whitley, 1997, as mentioned by Hanham, 2021). The results showed that neither ethnic nor gender groups significantly differed in their adoption of technology. This study contributes to our understanding of how different racial and gender groups perceive new information technology, which will benefit organizations trying to leverage their technology investment through knowledge sharing and developed communications that reduce user anxiety. Significant Difference in the Level of Technology Acceptance among College Students as Analyzed by Age Table 4 shows that there is no significant difference in UM Digos College student’s technological acceptance in terms of age, F(2,201) = 2.080, p = .128. This indicates that the students, regardless of age, have similar levels of technological acceptance. Hence, the result fails to reject the null hypothesis. It implies that similar levels of technical competence are observed over the years. Further investigations have demonstrated the beneficial effects of an age-related component in learning software programs (Morris et al., 2005, as quoted by Venkatesh et al., 2018). Furthermore, a study by Wang et al. (2009)-cited by Terblanche & Kidd (2022)-found no evidence of a significant age difference in the relationship between performance expectancy and the perceived ease of use of an online learning system. Age did not significantly affect P.U.’s relationship to join in online communities, according to research by Chung et al. (2010), as reported by Shin et al. (2022). Table 4: Summary of ANOVA for differences on students’ level of technology acceptance when analyzed by age Indicators Sum of Squares df Mean Square F p Perceived Usefulness (PU) Between Groups .533 2 .267 .864 .423 Within Groups 62.378 202 .309 Total 62.911 204 Perceived Ease-of-Use (PEU) Between Groups 1.201 2 .600 2.795 .063 Within Groups 43.383 202 .215 Total 44.583 204 User Satisfaction (US) Between Groups .683 2 .341 2.179 .116 Within Groups 31.654 202 .157 Total 32.337 204 Pa ge 14 3 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 138-147, 2024 Furthermore, with respect to age-related variations in perceived quality, younger individuals (i.e., those between the ages of 19 and 25) should be more appreciative of the features, functionalities, and designs of websites. 19 to 25 year olds are therefore more likely to be tech aware. Younger adults are also anticipated to view online platforms as more user-friendly since they are more accustomed to and connected with the new communication technologies (Chung et al., 2010, as quoted by Shin et al., 2022). Significant Difference in the Level of Technology Acceptance among College Students as Analyzed by Year Level Table 5 shows that there is no significant difference in UM Digos College students’ technological acceptance in terms of year level, F(3,200) = 1.907, p = .130. This indicates that the students, regardless of year level, have similar levels of technological acceptance. The table shows first-year students have the lowest mean rank, followed by second-year, fourth-year, and third-year. It is true of the indicators of perceived Usefulness, user satisfaction, attribute of usability, and overall technology acceptance of the student;s. It may mean that third-year student’s have the highest level of technology acceptance compared to the other year levels among year levels because technology influences their academic activities in their classes since they were exposed to technology even before the pandemic. Note that these differences are also significantly found in the specified indicators. According to Inozu et al. (2010, quoted by Dincer, 2020), undergraduate college students utilize technology, but they could use it more wisely. These results demonstrated that student motivation to learn depends critically on the external computing environment (Chen, 2020). The aforementioned findings suggest that learners’ attitudes and motivation towards computer-based self-directed learning are influenced by their perception of the benefits they receive from technology use. It’s probably because the growing technological modernism in the educational scene presents challenges for them (Lai et al., 2016). Attribute of Usability (AU) Between Groups .858 2 .429 2.437 .090 Within Groups 35.543 202 .176 Total 36.401 204 Overall Between Groups .560 2 .280 2.080 .128 Within Groups 27.198 202 .135 Total 27.758 204 *p<0.05, ** p<0.01 Table 5: Summary of ANOVA for differences on students’ level of technology acceptance when analyzed by year level Indicators Sum of Squares df Mean Square F p Perceived Usefulness (PU) Between Groups .796 3 .265 .858 .464 Within Groups 62.116 201 .309 Total 62.911 204 Perceived Ease-of-Use (PEU) Between Groups .737 3 .246 1.126 .339 Within Groups 43.846 201 .218 Total 44.583 204 User Satisfaction (US) Between Groups .834 3 .278 1.775 .153 Within Groups 31.502 201 .157 Total 32.337 204 Attribute of Usability (AU) Between Groups 1.032 3 .344 1.954 .122 Within Groups 35.369 201 .176 Total 36.401 204 Overall Between Groups .768 3 .256 1.907 .130 Within Groups 26.989 201 .134 Total 27.758 204 *p<0.05, ** p<0.01 CONCLUSION According to the data, there is no discernible variation in the degree of technological adoption between males and girls or between different age groups based on sex. It simply means that students accepted technology at the same rate regardless of their age or gender. Furthermore, Pa ge 14 4 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 138-147, 2024 students utilized technology for online learning in the same way across all age groups and genders. Moreover, the results indicate a noteworthy distinction in the degree of technology use according to marital status and year level. In terms of year level, third-year students were more accepting of technology than fourth-year students. It suggests that the switch from in-person instruction to online learning happened earliest for third-year students. Fourth-year students, on the other hand, received the lowest degree of technological acceptability, indicating that they were still acclimating to the online learning environment. Finally, about the status of marriage, students who were married scored the lowest. It shows that the married students had a stronger interest in technology. Married students, on the other hand, had the lowest level of acceptance of technology since they were more likely to pursue their academic goals and their families while attending classes on campus. RECOMMENDATIONS After the researchers endeavor to gather data, collate, interpret, and draw conclusions, they develop the following recommendations. Among all the indicators, Perceived Ease-of-Use (PEU) obtained the lowest mean score; the researchers recommend conducting a seminar entitled “Blended Learning: The Emerging Technologies” for students to be able to know the significance of technology and to continue embracing technological advancement, especially during this time of the pandemic to be held in UM Digos Campus. Teachers should increase student understanding of blended learning environments’ advantages, expectations, and demands. Teachers should be required to provide students with accurate and reliable information from various campus sources, so students have well-informed judgments regarding the learning settings most suited to their learning. Additionally, they must encourage students to start their college careers with blended learning skills. Students Pupils might possess the tools and information required to take advantage of online and blended learning opportunities and use school technology to meet their academic goals. All off-campus students ought to be provided with the same information on the benefits, prerequisites, and standards of online or mixed learning environments. Students with poor, moderate, or nonexistent home internet connectivity will benefit from the provision of high-quality networks throughout campus. To help pupils acquire Institutions may also consider collaborating with neighborhood public libraries or other local Table 6: Proposed Capability Building Action Plan G oa ls / O bj ec tiv es Sp ec ifi c O bj ec tiv es St ra ge gi es /A C tiv iti es M ap pe d to D im en si on s T im e Fr am e B ud ge t B ud ge t So ur ce Pe rs on s In vo lv ed E xp ec te d O ut pu t 1. F ac ili ta te in st ru ct io n in th e ne w li te ra ci es th at h av e em er ge d as a re su lt of th e di gi ta l a ge To ta lk a bo ut in co rp or at in g m ul tim ed ia in to le ss on s an d pr es en ta tio ns . Se m in ar o n In co rp or at io n of M ul tim ed ia in to Le ss on s a nd P re se nt at io ns D im en sio n 1 (F un da m en ta l K no w le dg e) Fe br ua ry 2 3, 2 02 3 c/ o U M D C U M D C Sp ea ke r R es ou rc e Pa rt ic ip an ts Fa ci lit at e in st ru ct io n in th e ne w li te ra ci es th at h av e em er ge d as a re su lt of th e di gi ta l a ge 2. Im pl em en t t he cu rr ic ul um m et ho d an d te ch no lo gi ca l st ra te gi es To li st th e va rio us te ch no lo gi ca l ap pr oa ch es a nd st ra te gi es Se m in ar o n th e St ud en ts ’ L ea rn in g T hr ou gh T ec hn ol og ic al A pp ro ac he s a nd St ra te gi es D im en sio n 2 (I ns tr uc tio na l M et ho ds an d St ra te gi es ) Fe br ua ry 2 4, 2 02 3 c/ o U M D C U M D C Sp ea ke r R es ou rc e Pa rt ic ip an ts Im pl em en t th e cu rr ic ul um m et ho d an d te ch no lo gi ca l st ra te gi es 3. D em on st ra te le ar ne r m as te ry o f ex pe rt ise , s ki lls , a nd di gi ta l- ag e w or k. To sh ow m as te ry o f pl an ni ng a nd d es ig n Se m in ar o n Pl an ni ng an d D es ig n, a s w el l as a n E ffi ci en t D ig ita l E nv iro nm en t D im en sio n 3 (M an ag em en t i n us in g te ch no lo gy ) Fe br ua ry 2 5, 2 02 3 c/ o U M D C U M D C Sp ea ke r R es ou rc e Pa rt ic ip an ts D em on st ra te le ar ne r m as te ry o f ex pe rt ise , sk ill s, an d di gi ta l- ag e w or k. Pa ge 14 5 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 138-147, 2024 Table 7: Proposed Capability Building Series Activity Design I. Title Blended Learning: The Emerging Technologies II. Date And Venue February 23 – 25, 2023/ UMDC Gymnasium III. Participants Teachers, Students, and Administrators IV. Rationale Even though the technology is present in almost every aspect of our lives, communities, and homes, most schools lag in incorporating technology into classroom learning. Many schools are only beginning to explore technology's true potential for teaching and learning. In order to integrate technology into classroom instruction, basic computer skills and software programs must be taught in a separate computer class. V. Objectives This program aims to: 1. To talk about incorporating multimedia into lessons and presentations. 2. To list the various technological approaches and strategies 3. To show mastery of planning and design VI. Budgetary Requirements The UMDC allocation will determine the budget. The institution must ensure budget allocation for the Resource Speaker’s Honorarium and Materials and Logistics for the Seminar. Table 8: Seminar Matrix Day 1 Time Topic/Activities Responsible Person 8:30 – 9:00 AM Preliminaries Event Committee 9:00 – 10:30 AM Incorporation of Multimedia in Lesson and Presentations Speaker 1 10:30 – 11:30 AM Workshop Speaker 1 11:30 – 12:00 NN Closing Program Event Committee Day 2 8:30 – 9:00 AM Preliminaries Event Committee 9:00 – 10:30 AM Technological Approaches and Strategies in Students Learning Speaker 1 10:30 – 11:30 AM Workshop Speaker 1 11:30 – 12:00 NN Closing Program Event Committee Day 3 8:30 – 9:00 AM Preliminaries Event Committee 9:00 – 10:30 AM Planning and Designing and the Effective Digital Environment Speaker 1 10:30 – 11:30 AM Workshop Speaker 1 11:30 – 12:00 NN Closing Program Event Committee REFERENCES Aburagaga, R., & Agoyoyi, K. (2020). Assessing faculty’s use of social network tools in Libyan higher education via a technology acceptance model. Alraimi, K. (2016). Understanding the MOOCs continuance: The role of openness and reputation. 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