670 Copyright © 2023 The Author IDEAS is licensed under CC-BY-SA 4.0 License Issued by English study program of IAIN Palopo IDEAS Journal of Language Teaching and Learning, Linguistics and Literature ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) Volume 11, Number 1, June 2023 pp. 670 - 685 Investigating Students’ Online Learning Readiness (SOLR) in Developing Students’ Soft Skills: A Survey at English Education Study Program (EESP), Faculty of Teacher Training and Education (FKIP), Jambi University Drs. Adrefiza, MA, PhD (adrefiza@unja.ac.id) Bunga AyuWulandary, SPd, MA, PhD (bunga_ad@yahoo.com) Received: 2023-07-26 Accepted: 2023-07-26 DOI: 10.24256/ideas.v11i1.3824 Abstract: Studies on Student Online Learning Readiness (SOLR) have been proliferating since the outbreak of the Covid-19 pandemic world-widely. The results seem to vary accordingly in terms of students’ levels, complexity, and contexts of its implementation across regions and countries. Through an internet survey questionnaire, involving 139EESP students at the faculty of education, at Jambi University, this study investigates the levels of SOLR and how they contribute to the improvement of the students’ soft skills. Adapting Yu and Richardson’s (2015) model, the results show a relatively high SOLR level with an average score of (3.64) (out of the maximum 5.0) in three aspects (technical, social, and communication). Technical competencies were the highest level with an average score of (3.70) and Communication appear to be the lowest (3.59) in the evaluation. The study also proves that all aspects of SOLR contributed significantly to the improvement of the students’ softs kills (self-regulated, critical thinking, problem-solving, and time management) with an average score of (2.8) (out of 4.0). Despite the pessimistic claims in its implementation, the findings have displayed a reverse phenomenon, proving the fact that the student’s readiness at EESP along with its contribution to students’ soft skills far outweighs the expectation. Online learning, although, with several limitations, should be implemented wisely and thoroughly to obtain maximum results. I. Background The implementation of online learning at schools and tertiary education has been tremendously popular in the last two years during the pandemic of Covid-19 around the globe. The dramatic shift from a conventional face-to-face towards a virtual platform in serving teaching and learning is apparent in almost all levels of education, starting from the lowest up to the highest ones such as at tertiary or universities. Such a move has brought significant impacts on various factors in educational practices. These include http://u.lipi.go.id/1457703302 mailto:adref64@yahoo.co.id mailto:bunga_ad@yahoo.com IDEAS, Vol. 11, No. 1, June 2023 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 671 planning, facilities, teachers, students, government, evaluation, resources, media, technology, pedagogies, and methods of teaching and learning. The impetus of implementing online learning is intended to control the spread of coronavirus (Covid-19) at schools or any other workplaces of education around the world. However, the realization of online teaching and learning is not without drawbacks. The effectiveness of its implementation varies from school to school and from university to university, depending on their readiness capacity. In many countries such as in modern and developed countries, the implementation does not seem to face a lot of problems. However, in many countries, especially in Indonesia, where supporting facilities are still limited and lacking, the implementation should be given indispensable attention from all parties including the relevant government authorities since the outcomes, results and effectiveness remain controversial and are still far from expectation. One of the most essential factors in the effectiveness of online learning operations is often linked to student readiness. Many studies such as (Liu, 2019; Seaman, Allen, & Seaman, 2018; and Kauffman (2015) suggest that student readiness in online learning plays an important role in accelerating learning achievement and success. This is unsurprisingly true because student readiness determines the level of students’ holistic mental and physical preparations and affects their learning performance. In addition, Blankenship and Atkinson (2017) claim that students' online learning readiness has affected educational institutions in almost all aspects such as in curriculum development, pedagogies, and academic atmospheres. This paper aims at investigating student online learning readiness (SOLR) and its contribution to the development of the students’ soft skills at EESP Jambi University. This study is critically important and significant for Jambi University’s (Unja) future online learning platform. As the outbreak of the Covid-19 pandemic in the country has not yet completely ended, the implementation of online learning is likely to be continued as an attempt to slowdown and stop the spread of the virus on campus. The study is also considered useful in obtaining information about student readiness concerning the improvement of the students’ soft skills. The results will hopefully be useful for the EESP in its efforts to improve the students’ soft skills which will, in turn, help increase their work readiness later when they complete their study. II. Theoretical Framework 2.1 Online Learning The pandemic of Covid-19 has changed the practice of educational implementation around the world. There has been a remarkable shift from face-to-face conventional towards virtual or online learning. Apart from its advantages, online learning is also viewed to have some limitations. Its effectiveness has been continuously evaluated since its popular practice, especially during the pandemic – all were conducted for better implementations. Despite its drawbacks, online learning has also been regarded as a fundamental part of the curriculum and instructional development at both schools and Higher Education Institutions (HEIs) (Liu, 2019:42). This is likely possible due to its pedagogical feasibility and ubiquity of accessing the information and communication, as well as the flexibility of time and space of learning. Online Learning Consortium (OLC) (2018) suggests that there has been a stable increase in online education in HEIs in the last two years. The increase is said to remain steady by 30% as the students were inclined to undertake the courses on an online basis (Seaman, Allen, & Seaman, 2018). Kauffman (2015) argues that online learning provides both opportunities and challenges for students. This is attributable, as claimed by Kauffman, to its self-regulated Drs. Adrefiza, MA, PhD&Bunga AyuWulandary, SPd, MA, PhD Investigating Students’ Online Learning Readiness (SOLR) in Developing Students’ Soft Skills: A Survey at English Education Study Program (EESP), Faculty of Teacher Training and Education (FKIP), Jambi University 672 nature and the distance between the instructors and the students which enable the students to experience different learning dimensions from the one they usually encounter in conventional learning. However, according to Kaymak and Horzum (2013), online learning may also present discouraging obstacles because of its limitations such as its dependency on technologies and media for accessing instructional content and instructors; the distance between students and instructors; the distance between students and their classmates; and the needs to adjust online discourse and interaction. Therefore, much research has been currently focused on finding effective ways to prepare students to successfully fulfil their learning performance in the online environment (Chan, 2017; Lieberman, 2017). 2.2 Online Learning Readiness In general, online learning readiness is defined as “cognitive awareness and maturity that a student develops for successful learning in a Web-based environment” (Liu, 2019). According to (Liu & Roberts-Kaye, 2016, p. 242), SOLR demonstrates the student’s intentional attempts in performing self-directed spirit, formulating learning strategies, acquiring technology competencies, adapting to digital protocols, and being open to instructional guidance. These intentional efforts are claimed to be an integral part of competency development that is vital to online learning success (Horzum, Kaymak, & Gungoren, 2015; Yukselturk & Bulut, 2007). Online learning readiness is often linked to students’ motivation and academic achievement (Liu, 2019). They, even, are said to be interrelated from one another. Furthermore, recent studies such as Mosa, Mahrin, & Ibrahim (2016); Blankeship & Atkinson (2017); and Yilmaz (2017) have proven that students’ online learning readiness (SOLR) has a considerable impact on their academic achievement. This is possible since the success of the online learning implementation depends largely on the readiness of both students and instructors with the support of the institutions. Blankenship and Atkinson (2017) further claim that SOLR affects most institutions not only in curricular development and pedagogies but also in the whole academic sector. A study from Horzum, Kaymak, and Gungoren (2015), involving 420 students in online learning from Sakarya University, reports that SOLR could predict motivation and perception of academic achievement. Another study by Cigdem and Ozturk (2016), reported by Liu (2019: 44) also proved that there was a strong link between self- direction online learning with student academic achievement. Yilmaz (2017), through a study which involved 236 undergraduate students in a flipped class, furthermore, suggests a similar finding, claiming that online learning readiness significantly influenced student satisfaction. 2.3 Online Learning Readiness Criteria The criteria of online learning readiness embody at least four main constructs (Yu & Richardson, 2015; Yu, 2018). These include (a) technical competencies; (b) social competencies with the instructor; (c) social competencies with classmates; and (d) communication competencies. These constructs have been referred to a recent SOLR model which is developed based on Tinto’s (1975) Student Integration Model (SIM). In Yu and Richardson’s (2015) and Yu’s (2018) model, the constructs of SOLR have been expanded into several (20) related items. The detail is shown in the following table. IDEAS, Vol. 11, No. 1, June 2023 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 673 Table 1: Yu and Richardson’s Construct (2015) Model of SOLR Construct Coding of Analysis Items Technical competencies TechComp 1 TechComp2 TcchComp3 TechComp4 TcchComp5 TechComp6 I have a sense of self- confidence in using computer technologies for specific tasks. 1 am proficient in using a wide variety of computer technologies. 1 feel comfortable using computers. 1 can explain the benefits of using computer technologies in learning. I am competent at integrating computer technologies into my learning activities. I am motivated to get more involved in learning activities when using computer technologies Social competencies with instructor SocialComp- Instructorl SocialComp- Instructor2 SocialComp- Instructors3 Clearly ask my instructor questions. Initiate discussions Drs. Adrefiza, MA, PhD&Bunga AyuWulandary, SPd, MA, PhD Investigating Students’ Online Learning Readiness (SOLR) in Developing Students’ Soft Skills: A Survey at English Education Study Program (EESP), Faculty of Teacher Training and Education (FKIP), Jambi University 674 SocialComp- Instructor4 SocialComp- Instructor5 with the instructor. Seek help from the instructor when needed. Timely inform the instructor when unexpected situations arise. Express my opinions to the instructor respectfully. Social competencies with classmates SocialCompPeersI SocialCompPecrs2 SocialCompPeers3 SocialCompPeers4 SociaICompPeers5 Develop friendships with my classmates. Pay attention to other students’ social actions. Apply different social interaction skills depending on the situation. Initiate social interaction with classmates. Socially interact with other students with respect. IDEAS, Vol. 11, No. 1, June 2023 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 675 Communication competencies CommCompl CommComp2 CommComp3 CommComp4 1 am comfortable expressing my opinion in writing to others. I am comfortable responding to other people’s ideas. I am able to express my opinion in writing so that others understand what I mean. I give constructive and proactive feedback to others even when 1 disagree. SOLR Questionnaire (Yu & Richardson, 2015; Yu, 2018) The above SOLR instrument, according to Liu (2019) lies primarily on its social aspects, especially in identifying students' sense of social belonging in online learning environments. The four constructs with 20 items in the above SOLR instrument have been further studied by a few researchers such as Kim Kwon & Cho. (2011); Lee (2014); and Moore & Kearsley, (2011). Most of the results reveal that “proper scaffolding with supportive courses or programs can build student preparedness and strengthened readiness for online learning” (Cigdem & Ozturk, 2016; Horzum, KaymakGungoren, 2015; Kauffman, 2015; Liu & Adams, 2017), as reported by Liu (2019: 45). 2.4 Soft-Skills Many studies such as those (Schulz, 2008 and Zhang, 2012) have suggested that soft skills are important aspects inherited in students learning especially in tertiary education or HEIs. Soft skills are said to complement hard skills which contribute essentially to the student’s success both in their studies and in careers. The lack of soft skills among university graduates has long become a major complaint among employers across the globe in the last few decades (Schultz, 2008). Apart from hard skills, these generate components are said to play a vital role in the learner's success. A general concept of soft skill is described on the Google website-that is anything that relates to how someone works. These skills include interpersonal, communication, listening, and time management (https://www.google.com/search?client=firefox-b-d&q=soft+skills). It is claimed on this website that such skills determine the quality of someone in the workplace. Drs. Adrefiza, MA, PhD&Bunga AyuWulandary, SPd, MA, PhD Investigating Students’ Online Learning Readiness (SOLR) in Developing Students’ Soft Skills: A Survey at English Education Study Program (EESP), Faculty of Teacher Training and Education (FKIP), Jambi University 676 Wikipedia provides a more detailed concept of soft skills. It is stated that these skills represent several essential talents that are pertinent to someone’s capability at work. These include, for instance, social skills, communication skills, personality traits, attitudes, social, and emotional intelligence. With these skills, according to Wikipedia, people will be able to better cooperate with others, manage their environment, and perform well to achieve their goals. Schulz (2008) provides more elaborate components of soft skills. In his description, soft skills include other aspects such as problem-solving, self-management, responsibility, negotiating skills, conflict management, cultural awareness, empathy, and many more social aspects. For details, see the following table. Table 2: Soft Skills Components (Shultz, 2008: 147) A study by Zhang (2012) reports that both dimensions of hard skills and soft skills are useful to increase the student’s ability to communicate, resolve conflicts, and bring different functions together toward a common goal. It is recommended that professional educators should promote soft skill training in an active learning environment to achieve success in education. This study investigated a peer assessment on the use of both soft and hard skills in IT education and training. III. Methods 3.1. Research Design This study adopts quantitative descriptive methods in the form of survey studies through two main variables: SOLR and SOFT SKILLS of EESP students, FKIP Jambi University. A simple quantitative measure was intended to collect data or information from selected respondents who were studying at the undergraduate level, at EEP the Faculty of Teacher Training and Education (FKIP), Jambi University. The survey was intended to gain the students' perception and evaluation of how they scored themselves (using 4 Likert scales) in their soft skillslevels and development during the implementation of online learning. IDEAS, Vol. 11, No. 1, June 2023 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 677 Chart 1: Variables of the Study. As seen in the above chart, the first main variable (SOLR) is divided into three main sub-variables (Technical Competencies; Social Competencies; and Communication Competencies). The second main variable (SOFT-SKILLS) is divided into four sub-variables (Self-Regulated Learning; Critical Thinking; Problem-Solving; and Time Management). Both main variables are scaled using Likert scales, and finally, the respondents’ self-evaluation of their soft skills development was matched with their SOLR levels to find out if there are improvements intheir soft skills due to their SOLR levels during the implementation of online teaching and learning practices. 4.2 Population and Sample The study involved139 EEP FKIP students selected randomly and purposively based on their grades and semesters. The highest priority was given to students who werein their three-final semesters – gender and age werenot taken into account as the study does not focus on seeking the disperse of the data according to these variables. 4.3 Instruments The first part of the instrument of the study was based on Yu and Richardson’s (2017) SOLR model with a few modifications. The modifications were intended to make a necessary adjustment according to the local contexts and characteristics of the respondents. This instrument was meant to gain information about the level of SOLR among respondents. The second part of the instrument was based on the Soft-Skill performance of the respondents which wascategorized into four areas (self-regulated learning, critical thinking, problem- solving, and time management). The respondents were asked to self-evaluate their options, using Likert scales. Both parts were designed through several questionnaires which were sent electronically through the internet to 139 selected respondents for data collection. 4.4 Data analysis Data were analyzed using simple statistics in two main measurements. First, following Yu and Richardson’s (2017) SOLR model, the data were identified, grouped, tabulated, and analyzed quantitatively. All data were based on the respondents’ self- evaluation of their SOLR levels (using 5 Likert scales: 1-Strongly Disagree; 2-Disagree; 3- Neutral; 4-Agree; and 5-Strongly Agree). The second data variable (the students’ soft skills improvement or development) wasalso identified, grouped, tabulated, and analyzed quantitatively through the students’ responses (self-evaluation),using 4 series of Likert scale questionnaires (1-Very Low; 2-Low; 3-High; and 4-Very High). Both data were paired and matched to see if there was a significant improvement or development of the students’ soft Drs. Adrefiza, MA, PhD&Bunga AyuWulandary, SPd, MA, PhD Investigating Students’ Online Learning Readiness (SOLR) in Developing Students’ Soft Skills: A Survey at English Education Study Program (EESP), Faculty of Teacher Training and Education (FKIP), Jambi University 678 skills as a result of their SOLR levels. V. FINDINGS AND DISCUSSION 5.1SOLR Levels The first critical result of data analysis shows that the SOLR level is high. With an average score of (3.64) the student’s online learning readiness in three main competencies (technical, social, and communication) reaches an astonishing level which may be previously overlooked and underestimated since it was due to its efforts to control the spread of Covid-19 on campus. It was highly presumed that the implementation of online learning at this tertiary institution would face many problems including student and lecturer readiness. The results, surprisingly, show a reverse phenomenon where the students’ readiness was relatively high. The detail can be seen in the following table: Table 3: SOLR Score Distribution As seen in the above table, students’ readiness in three areas remains stably high (all above (3.5) in rate distribution. The technical aspect which may be previouslyunderestimated due to an assumption that either the students or the institution is normally deemed not to have a strong preparation and readiness for online practice, appears to show a reverse phenomenon. This aspect turns out to be the highest in rate (3.7) out of (5.0) maximum score. Conversely, students’ readiness in Communication competencies appears to be the lowest rate, whileSocial competencies were rated (3.65) right in the middle position. This is not in line with what Kaymak and Horzum (2013) claim that online learning could present daunting barriers due to its limitations in IT support. Detailed data distribution can be seen in Table 4 below: 3.7 3.65 3.59 3.64 TECHNICAL SOCIAL COMMUNICATION AVERAGE SOLR SCORE DISTRIBUTION IDEAS, Vol. 11, No. 1, June 2023 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 679 Table 4: Detailed SOLR Item Score Distribution SOLR NO ITEMS 1 2 3 4 5 TOTAL AVE TECHNICAL 1 I have a sense of self-confidence in using computer technologies for specific tasks 1 5 39 77 17 139 3,74 2 1 am proficient in using a wide variety of computer technologies 1 12 65 51 10 139 3,41 3 1 feel comfortable using computers 2 7 21 72 37 139 3,97 4 1 can explain the benefits of using computer technologies in learning 1 5 42 77 14 139 3,7 5 I am competent at integrating computer technologies into my learning activities 1 3 59 65 11 139 3,58 6 I am motivated to get more involved in learning activities when using computer technologies 3 3 38 73 22 139 3,77 AVERAGE 3,70 SOCIAL 7 Clearly ask my instructor questions 3 8 70 50 8 139 3,37 8 Initiate discussions with the instructor 0 13 65 58 3 139 3,36 9 Seek help from the instructor when needed 3 3 30 81 22 139 3,83 10 Timely inform the instructor when unexpected situations arise 1 1 45 72 20 139 3,78 11 Express my opinions to the instructor respectfully 2 3 31 80 23 139 2,99 12 Develop friendships with my classmates 3 6 16 77 37 139 4 13 Pay attention to other students’ social actions 2 6 36 80 15 139 3,71 14 I am able to express my opinion in writing so that others understand what I mean 1 3 49 72 14 139 3,68 15 Initiate discussions with classmates 2 5 36 73 23 139 3,79 16 Socially interact with other students with respect 2 4 22 76 35 139 3,99 AVERAGE 3,65 COMM 17 1 am comfortable expressing my opinion in writing to others 2 8 46 68 15 139 3,61 18 I am comfortable responding to other people’s ideas 1 4 56 66 12 139 3,6 19 I am able to express my opinion in writing so that others understand what I mean 2 1 44 72 20 139 3,76 20 I give constructive and proactive feedback to others even when 1 disagree 2 7 74 49 7 139 3,37 AVERAGE 3,59 TOTAL AVERAGE 3,64 5.1.2.Technical Competencies - Using Computer Technologies In general, the data depicting students’ online learning readiness in Technical Competencies shows a surprisingly high rate of student evaluation. This competence refers to the student’s awareness of the use of computer technologies during online learning. As shown in Table 4 (items 1 – 6), the majority of respondents’ claims fall into Strongly Agree, Agree, and Drs. Adrefiza, MA, PhD&Bunga AyuWulandary, SPd, MA, PhD Investigating Students’ Online Learning Readiness (SOLR) in Developing Students’ Soft Skills: A Survey at English Education Study Program (EESP), Faculty of Teacher Training and Education (FKIP), Jambi University 680 Natural options, with only a small proportion lying on negative choices such as Disagree and Strongly Disagree. From data distributions, it can be drawn that the implementation of online learning at the English Education Program (EEP) with the use of computer technology did not seem to be a significant problem for many students.In other words, they are capable enough to cope with such a computer technological matter and development. More significant positive claims can be seen in the students’ self-confidence in using computers (item 1), their comfortable feeling in using computers (item 3), and their motivation to get more involved in learning activities (item 6). The latest phenomena are interesting to observe as this aspect displays a usual trend because online learning has been often regarded to reduce the students’ collaboration and cooperation in learning. The other criteria such as those illustrated in items 2, 4, and 5 are relatively moderate in the distributions, but they still demonstrate positively high rates. Such an above phenomenon does not seem to comply with previous claims such as Kaymak and Horzum (2013), where the efficacy of online learning has been often questioned as its operation frequently depends on technological support. However, the findings of the present study which is seen in the above data have proven something in reverse. It may be realized that the success of online learning can be affected by numerous factors, one of which may refer to student motivation and readiness. 5.1.3.SocialCompetencies Concerning Social Competencies, it is evident in Table 4 that all items (7-16) are generally rated high by the respondents. The highest rate was addressed to item 12 (developing friendships with classmates) with an average score of (4.0), while the lowest rate (2.99) was given to item 11 (expressing an opinion to the instructor respectfully). Students' online learning readiness in social aspects provides a relatively challenging trend. In general, the data depicting students’ online learning readiness in classroom interaction shows a positive trend. As shown in Table 4, the majority of respondents’ evaluation falls into Agree and Natural options, with very small proportions lyingin Disagree and Strongly Disagree categories. This further convinces the fact that the student’s readiness in this aspect was relatively high, and they did not seem to have significant barriers in social interactions during online learning. 5.1.4.CommunicationCompetencies Although students’ readiness in Communication competencies was the lowest in the evaluation rates, its trend remains convincing with an average score of (3.59). The distribution in four items looks flat with the ability to express ideas in writing appearing to be the highest rate (3.79), while the ability to give constructive and proactive feedback was rated the lowest one (3.37). There is nothing much that can be elaborated on this phenomenon, but the students might find it a bit hard to develop their communication skills through online learning because they did not get used to a situation where social interaction was done through electronic media. However, its rate still looks convincing, displaying a relatively high readiness level. The above findings have proven the fact that SOLR and student learning performance are interrelated. As suggested by Liu & Roberts-Kaye (2016), SOLR reflects the student’s intentional attempts in presenting self-directed courage, formulate learning strategies, develop technology competencies, adapt to digital procedures, and be open to instructional guidance. It is evident in the present study IDEAS, Vol. 11, No. 1, June 2023 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 681 that SOLR was rated relatively high in three main aspects (technology, social, and communication), proving that the unexpected shift of learning from conventional to online platforms did not make students appalled, demotivated, and unprepared. The results have shown a high level of readiness for online learning and this readiness is said to reflect high intentional efforts which are claimed to be an integral part of competency development that is vital to students’ learning success and academic achievement (Horzum, Kaymak, &Gungoren, 2015; Yukselturk&Bulut, 2007; Blankenship & Atkinson, 2017; Yilmaz, 2017; and Liu, 2019). 5.2. Soft Skills Levels Students’ self-evaluation of their soft skills levels during online learning shows an interesting trend. With an average score of (2.80) out of (4.0) maximum score, the posture of the evaluation remains high in rate and shows a relatively high and convincing result. As can be seen in Table 5 that Problem Solving was the highest in the evaluation rate with a total score of (2.88), while Time Management and Critical Thinking were the lowest with scores of (2.75) each. Self-regulated appears in the middle with a score of (2.80). The detailed distribution can be seen in Table 5 below: Table5: Students’ Soft Skills Evaluation The data distribution in the above table displays an interesting trend. During online learning, the students were highly exposed to maintaining their soft skills at maximum levels. This could be done through several challenging tasks and learning activities such as finding digital resources for answering and solving problems or cases provided by lecturers. The students would have to critically search, find, observe, and analyze facts, information, and data from various sources often from the internet to answer and solve the cases and problems in a set period. This certainly makes them creative and maintains their soft skills, especially in the above areas (Time Management, Problem Solving, Critical Thinking, and Self-regulated). Such exposure might not be as effective as in offline learning where students lie very much on the lecturers. 2.8 2.75 2.88 2.75 2.80 SELF-REGULATED CRITICAL THINKING PROBLEM SOLVING TIME MANAGEMENT AVERAGE SOFT SKILLS IMPROVEMENT Drs. Adrefiza, MA, PhD&Bunga AyuWulandary, SPd, MA, PhD Investigating Students’ Online Learning Readiness (SOLR) in Developing Students’ Soft Skills: A Survey at English Education Study Program (EESP), Faculty of Teacher Training and Education (FKIP), Jambi University 682 Table6: SOLR and SOFT SKILLS IMPROVEMENT SOLR SOFT SKILLS SELF-REGULATED CRITICAL THINKING PROBLEM-SOLVING TIME MANAGEMENT Options 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 TECHNICALS 0 26 108 5 0 44 88 7 0 20 111 8 2 56 62 19 AVERAGE 2,84 2,73 2,91 2,7 SOCIAL 1 38 94 6 1 45 86 7 0 31 99 9 2 46 74 17 AVERAGE 2,75 2,71 2,91 2,76 COMM 0 32 100 7 0 31 103 5 0 33 96 10 2 40 81 16 AVERAGE 2,82 2,81 2,83 2,79 TOTAL AVERAGE 2,8 2,75 2,88 2,75 It can be seen in Table 6 above that SOLR and students’ soft skills are interconnected in the students’ self-evaluation. A relatively high rate of soft skills evaluation was addressed to the High category and very little proportion in the Low and Very Low categories. The trend signals that the students’ soft skills highly improve during online learning due to their SOLR levels. Although the score distribution of the four soft skills looks flat in proportion, Problem Solving was the highest in rate with an average score of (2.88), while Critical Thinking and Time Management were the lowest ones in rate. There is nothing much that can be pointed out here, but the interconnection between SOLR and soft skills indicates that students’ readiness in online learning and the development and improvement of their soft skills can be simultaneously enhanced to accelerate the teaching and learning outcomes. Despite many claims that student soft skills cannot be effectively maintained through online learning, this present study has shown a reverse trend. Such a pessimistic claim does not exist in this study. Students’ self-evaluation has proven that their soft skills development was rated high along with their SOLR levels. This is in line with what Benigno and Trentin (2000) state that compared with offline learning, online learning has greater flexibility along with a wider range of stimuli and resources that can be offered to the students. This capacity, according toBenigno and Trentin, increases the students’ freedom and creativity as well as develops their interest in determining their learning path, using the available learning materials provided by the instructor. Such a capability would be attributable to the development of students’ soft skills. They further claim that online learning is largely based on collaborative learning models so that the students’ collaboration can be maintained and developed. In addition, participant interaction is said to be the key element in online learning, and the evaluationentails some of the indicators that are typically adopted for the evaluation of face-to-face courses so that theyarestrictly linked to the sociality of the IDEAS, Vol. 11, No. 1, June 2023 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 683 learning process. VI. CONCLUSION AND RECOMMENDATION Based on data collection and analysis, it is obvious that the students’ self- evaluation of their online learning readiness in three main aspects: (1) technical, (2) social, and (3) communication competencies show positive trends. The student’s readiness in these three aspects remains high with an average score of (3.64) out of a total (of 5.0). This reveals that many students felt that they did not have any significant problems and were prepared in pursuing online learning which was implemented as an effort to stop the spread of Covid-19 on campus. Despite the claim that online learning is not without limitations in terms of technical support and human resource readiness, its implementation at Jambi University, particularly in English Education Study Program (EESP) such a limitation did not exist, and the students did not find it as a serious problem which may hamper their learning readiness. The dramatic shift from a conventional face-to-face to a virtual platformfor serving to teach and learningin the last few semestershas not been a major problem to many students in terms of readiness. Even though its implementation was heavily burdened with preparatory actions at the beginning, it has been progressing significantly well and all the pessimistic claims regarding the implementation did not prevail. Furthermore, it is evident in the study that the students’ online learning readiness has been regarded to have a high contribution towards their soft-skills improvement. This may contradict the claims that soft skills can only be enhanced optimally and effectively only through offline learning. Such a claim, however, was not evident in the present study. The students’ self-claim shows a reverse fact and they believed that they could still enhance and accelerate their soft skills through online learning. As shown in the data, the contribution of all soft skills such as self-regulated learning skills, problem- solving skills, critical thinking skills, and time management skills remained relatively high. Again, such a result may contradict the claims that online learning does not effectively support the students’ soft-skill development as it does in offline learning. However, the student’s responses in the present study have proven something on the other side. It is expected that the results of this study provide useful insights for a better implementation of online teaching and learning at Jambi University in general, particularly at EESP. To have a higher generalization of the study, it is recommended that the study will be conducted in other study programs to see the variations in results and practices so that a general picture of its realization can be documented for future improvements.It is realized that the realization of online teaching and learning varies from school to school and from university to university, depending on their readiness capacity. It may be worthwhile to look at how the implementation affects the students’ achievement and compare it with the results obtained through offline or blended learning platforms. Such a comparative study is deemed useful for making proper decisions in the future when the pandemic of Covid-19 still prevails. REFERENCES Drs. 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