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Abstract

This study explored online assessment and highlighted significant assessment strategies, such as capstone projects, quizzes, and self-assessments. Inquiry-based assessment, transformative assessment, and scaffolding with self-assessment resources formed the framework. Our goal was to determine the effects of certain self-assessment parameters on student achievement in online learning, including the frequency of answering online self-assessment questions, the points earned for correct answers, and the time spent answering questions. Data from undergraduate geoscience courses were analyzed using a hierarchical linear model, and the results showed that frequently answering online self-assessment questions is a strong predictor of higher final grades, capstone project success, and quiz results. The frequency of answering online self-assessment questions was a more significant factor than the amount of time spent on these questions and the total points earned. Educators are encouraged to redesign their courses to include self-assessment resources that promote frequent student engagement instead of just one-time interaction.

Keywords

online assessment assessment strategies self-assessment student success capstone project

Introduction

Today's students have access to academic programs and courses from anywhere in the world due to the noteworthy rise in popularity of online education. Similar to traditional education, online education relies heavily on assessment via reliable means to gauge student learning. Assessments, whether conducted online or in an in-person setting, are critically important to offering educational quality and equity (Meyer et al., 2010; Montenegro & Jankowski, 2020). To address possible disparities that can emerge in online learning environments, it is essential to examine and improve current assessment means as we work toward a more inclusive educational landscape. Exams, capstone projects, and a variety of other assessment formats can all be used to evaluate student success in different ways. For instance, quizzes can be used to assess students’ retention of knowledge (El-Hashash, 2022), and capstone projects provide students a chance to apply what they have learned (Basholli et al., 2013). It is critical to use different assessment methods in balance in online education since they provide a thorough and all-encompassing way to gauge student learning (Arend, 2007; Millet & Dinç, 2023).

A diverse group of students may now learn in ways that offer flexibility and convenience thanks to online education. However, there are still issues with equal access to high-quality education (Alenezi et al., 2023; Gu, 2021). Assessments take on additional significance in this context. Learning outcomes for all students may be improved and inequities can be reduced by having an in-depth understanding of assessment creation and usage in online learning contexts (Coates, 2016; Fung et al., 2022). In this study, we concentrated on quizzes, capstone projects, and self-assessment, reflecting actions of remember-understand, evaluate-create, and retrieval practice, respectively (Millet & Dinç, 2023).

While quizzes allow for frequent engagement with the course content (Argyriou et al., 2022), capstone projects accommodate investigating real-world problems and integrating theory and practice (Basholli et al., 2013). Quizzes and capstone project performances both are significant predictors of success in an online course (Figueroa-Canas & Sancho-Vinuesa, 2020). Self-assessment questions are another means of assessment that contribute to student learning. Self-assessments allow for reflection on learning, critical thinking, and strength and weakness awareness through monitoring (Conrad & Openo, 2018). Factors related to self-assessment can be investigated, such as frequency, points earned, and time spent. For example, the frequency of answering self-assessment questions is positively correlated with student success in quizzes and capstone projects (Millet et al., 2022), and it also serves as a significant predictor of quiz success (Dinç & Millet, 2022). With that in mind, we sought answers to our research question: How do (a) the frequency of answering, (b) the total points earned from correctly answered, and (c) the time spent on self-assessment questions influence student success in quizzes, capstone projects, and overall course grades?

Multifaceted Assessment Strategies for Student Learning

Diverse assessment methods comprise a variety of evaluative modalities, including quizzes, capstone projects, and self-assessment. Various assessments exhibit students’ understanding and competencies, enabling them to demonstrate their knowledge and skills in multiple ways. Quizzes, for example, allow for acquisition of facts in the content and serve as a feedback mechanism during the learning process (Salas-Morera et al., 2012; Sewell et al., 2010). Assessment methods, such as capstone projects, facilitate the application of knowledge to real-world problems and events, fostering a more authentic educational experience (Maleki, 2009; Zhang et al., 2021).

Students conceptualize self-assessment as a regulatory learning strategy as self-regulated learning is dependent on its two components: self-monitoring and self-evaluation (Panadero et al., 2017; Zimmerman & Labuhn, 2012). Self-assessment requires students to assess their own thinking and cognitive processes, indicating metacognitive awareness and responsibility for the learning process (Mok et al., 2006). Through self-assessment, timely and delayed feedback, a critical element in online education, enriches students’ learning performances, empowering them to identify areas that need improvement and adapt their learning strategies (Ifenthaler et al., 2023; Thomas et al., 2017). In addition, self-assessment significantly influences student learners’ self-efficacy and allows them to take ownership of learning (Panadero et al., 2017). The meta-analysis conducted by Panadero and colleagues (2017) indicated a substantial effect size of 0.73 of self-assessment on self-efficacy. Sitzmann et al. (2010) identified strong correlations between self-assessment and motivation and satisfaction in both education and workplace settings. In addition, self-monitoring was detected to be a significant moderator of self-efficacy (Panadero et al., 2017). Self-assessment involves a wide variety of mechanisms and has been shown to improve learning's effectiveness and outcomes (Yan, 2020). Incorporation of rubrics and feedback with self-assessments further strengths its conducive effect to learning outcomes (Panadero & Jonsson, 2013; Panadero & Romero, 2014; Sitzmann et al., 2010). In parallel, data analytics in assessment, including self-assessment, provides data regarding students’ academic performance, enabling instructors to receive insights into students’ learning processes and make data-driven pedagogical adjustments (Baker & Siemens, 2022; Mattingly et al., 2012).

Transformative Assessment: Quizzes

Quizzes play a transformative role in education, influencing both teaching and learning. They offer a wide array of benefits beyond merely serving as assessment tools, contributing to the multifaceted landscape of education. Their inherent function as formative assessments guides students toward self-regulation and fosters continuous, consistent engagement with course content. Blanco and Ginovart (2012) emphasized the use of quizzes as low-stakes assessment activities, promoting self-regulation, and fostering an environment of ongoing learning, a characteristic of effective education. Online quizzes, as argued by Cook and Babon (2017) and Gamage et al. (2019), deserve special attention in the digital age. They function as robust tools, enhancing engagement, motivating students, and thus ensuring the enduring relevance of their academic efforts. The provision of automated feedback, a valuable feature of online learning platforms, proves beneficial in offering timely feedback into student performance. This feature not only streamlines the process but also represents a significant time and effort-saving aspect of instruction (El Said, 2021; Gamage et al., 2019).

Quizzes are distinguished by their diverse question types, catering to various learning needs and serving as a valuable resource for learning opportunities. Blanco and Ginovart (2012) underscored the importance of this diversity, encompassing a range of question formats, including multiple-choice, true/false, and short answers. These question structures in quizzes not only challenge students in diverse ways but also present significant opportunities for grasping factual knowledge (Enders et al., 2021). Additionally, the remote management features inherent in web-based assessment tools contribute to educational flexibility and effectiveness (DeSouza & Fleming, 2003). In the digital era, the favorable responses from students regarding quizzes (Chaqmaqchee, 2015; Gamulin & Gamulin, 2012) affirm the increasing significance of quizzes in education. Gamulin and Gamulin (2012), for instance, concentrated on the usage of online quizzes. Their research examined how formative assessments affected students’ preparation, motivation, and academic achievement. Their main conclusions showed that online quizzes improved students’ comprehension of the material and impacted how well they prepared for lab activities. The quizzes were intended to be formative, offering quick feedback and assisting in the process of learning. Their study looked at how the students perceived these quizzes in terms of their usefulness and simplicity of use. They indicated that using online quizzes to supplement classroom instruction can be a useful strategy for improving student learning and success.

Student feedback highlights that quizzes are not just assessment tools but also essential conduits for boosting student confidence and facilitating their progress in comprehending course content (Chaqmaqchee, 2015). Consequently, we can assert that quizzes are transformative educational instruments, facilitating learning, engagement, and motivation in educational settings. Quizzes are well-suited to integrate into assessment plans with other assessment types, including project-based assessments (Heil & Ifenthaler, 2023).

Inquiry-Based Assessment: Capstone Projects

Project-based assessment, such as capstone projects, embodies a dynamic and student-centered approach to learning. It departs from traditional teacher-centered instruction, encouraging students to take initiative using inquiry-based methods (Baker, 2006; Bell, 2010). Its congruence with Bloom's Taxonomy, encompassing creating a product and the development of higher-order thinking skills, is one of its notable strengths (Baker, 2006; Joo et al., 2019). The interdisciplinary character of project-based learning enables students to apply their knowledge and work together across a variety of fields, developing skills for addressing real-world problems (Joo et al., 2019; Kokotsaki et al., 2016). Further, the development of critical thinking skills and a deeper engagement with course content are advantages for students (Mitchell et al., 2019; Torrijo et al., 2021).

Project-based learning extends beyond the classroom, connecting academic content to authentic real-world problems (Bell, 2010; Mathews & Wikle, 2019). By placing an emphasis on soft skills including collaboration, practical thinking, and communication (Rice & Shannon, 2016; Torrijo et al., 2021), it prepares students for the challenges of the 21st century. However, there are obstacles, such as extensive content coverage in limited time and lack of creativity in capstone projects, adequate equipment, and guidance for students (Plotnick et al., 2009; Wan et al., 2018). To make sure that students have the necessary knowledge and skills before beginning self-directed projects, it is crucial to strike a balance between teacher's instruction and self-governed inquiry (Kokotsaki et al., 2016). The accumulative evolution of project-based assessment holds promises for fostering learning experiences that equip students with not only content knowledge but also the practical skills and competencies needed to solve problems (Joo et al., 2019; Lin, 2018; Mountrakis & Triantakonstantis, 2012). For example, Terrón-López et al. (2017) inquired about how to incorporate capstone projects, where students work on real-world problems, into the curriculum as part of a project-based learning approach. These initiatives aimed to increase student motivation, enhance learning, and foster the growth of critical problem-solving, teamwork, and communication abilities. In their study, adoption of project-based learning showed encouraging outcomes for student motivation, skill development, and academic achievement.

Scaffolding with Self-Assessment Resources

Self-assessment can function as a scaffolding method. The term scaffolding refers to a supporting framework that guides students toward proficiency in learning activities (Quintana, 2021; Tabak & Kyza, 2018). This framework for self-assessment entails providing students with the tools, resources, and opportunities to evaluate their own learning critically. Self-assessments, particularly those completed online, provide as a “no-risk” setting for students to identify gaps in their understanding and receive immediate feedback (Ćukušić et al., 2014; Mok et al., 2006; Thomas et al., 2017, p. 1063). Active participation in self-assessment helps students grasp course content and the language of the subject matter, leading to a deeper understanding of the content (G. J. Thomas et al., 2011; J. A. Thomas et al., 2017). Papanthymou and Darra (2019) investigated the influence of self-assessment on numerous educational parameters. In order to investigate the ways in which self-assessment fosters self-esteem, self-regulating learning, academic achievement, and learning motivation, they examined 37 empirical research. Results from a variety of disciplines and educational levels showed that self-assessment has a beneficial impact on these domains. Their study underlined the value of self-assessment in educational settings and highlighted how it helps students achieve better learning outcomes and grow personally.

Self-assessment is a tool that can be embedded within the learning process where students autonomously make decisions about their learning with the use of assistance offered (Boud & Falchikov, 1989; Taras, 2010). This empowerment is consistent with the idea that students become more engaged and self-confident in their education, ensuring less dependence on instructors (Ćukušić et al., 2014; Millet et al., 2021). The positive correlations between exam scores and self-assessment points (Ćukušić et al., 2014) and between final grades and self-assessment engagement (Thomas et al., 2017) emphasize the effectiveness of self-assessment as a predictive measure of academic performance, further underlining its significance as a scaffolding method. According to Taras (2010), the self-assessment process, which involves critical reflection before, during, and after learning (Fallows & Chandramohan, 2001; Mok et al., 2006), encourages metacognition and equips students with skills they need for both personal and professional growth. Self-assessment essentially serves as a scaffolding method that stimulates student growth, promotes reflective practice, and supports the entire or part of the learning process.

Method

Participants and Setting

The participants in the study were all undergraduate students enrolled in three online geoscience courses (n₁= 280, n₂= 52, n₃= 64) at an R1 university in the United States.

The students completed the course as they normally would with no additional work or assessment required of them. The students all completed the online courses asynchronously with weekly deadlines to maintain similar pacing in each course. We collected and anonymized the data at the end of the semester using queries to extract assessment data on the students who consented to being part of the study from both the central learning management system (LMS) records and our content management system (CMS). From the LMS, we collected assessment scores on quizzes, capstones, and each student's final grades. From the CMS, we collected data on the online self-assessments.

Measures

The assessment of student success in each course involved a combination of quizzes and capstone projects, which served as common evaluation tools. These assessment components were quantified as continuous variables. Additionally, the overall course success was measured using final scores, also treated as a continuous variable.

Our study examined independent variables related to online self-assessment factors. These included:

Frequency of answering online self-assessment questions: This variable captured the attempt at which students engaged with online self-assessment questions by answering the questions.

Points earned for correctly answered online self-assessment questions: This variable represented the number of points awarded to students for providing correct answers to online self-assessment questions. One point was given for each correctly answered question. Students were informed in the beginning that these points will not be aggregated into the final score in the course.

Time spent on online self-assessment questions: This variable measured the amount of time students dedicated to answering online self-assessment questions as an indicator of their self-paced learning efforts.

Data Analysis

The hierarchical linear model was used to gauge the effect of the frequency of completion of online self-assessment questions, total points for correctly answered online self-assessment questions, and the time spent on online self-assessment questions on students’ quiz grades, capstone project grades, and final course grades. Analysis was conducted in R with the lmerTest R package (Kuznetsova et al., 2017). The study variable was treated as a random effect in the hierarchical linear models to account for the fact that students are nested within each course section.

Results

Empirical evidence showed that completing the online self-assessment questions enhanced student success on capstone projects, quizzes, and final score. Frequency and total points for correctly answered self-assessment questions are both significant positive predictors for students’ final grade (see Table 1). For each additional attempt to complete the online self-assessment questions, the student's final grade increased by 0.171 point. For one additional point earned in the self-assessment questions, the student's final grade increased by 0.11 point. Although both frequency and points earned were detected as significant positive predictors for one's final grade in the course, frequency has a larger beta estimate than total points earned, indicating that frequent checks of one's understanding might play a more significant role in one's successful completion of the course than total points earned in these assessments.

For the capstone project grade, frequency is a significant positive predictor (see Table 2). For each additional attempt to complete the online self-assessment questions, the student's final grade increased by 0.173 point. The result demonstrated that the online self-assessments had a positive effect on students learning not only on the immediate assignment (e.g., quiz) but also on the assignment later on in the semester (e.g., capstone project).

For the quiz grade, all three variables: frequency, total points for correctly answered online self-assessment questions, and time spent, are all significant positive predictors (see Table 3). For each additional attempt to complete the online self-assessment questions, the student's quiz grades increased by 0.173 point. For one more point earned in the self-assessment questions, the student's quiz grade increased by 0.089 point. For each additional minute spent on the self-assessment questions, the student's quiz grade increased by 0.00005 point (not shown on the table due to the three decimal point limit). Among all three predictors, frequency again has the highest beta estimate, indicating the conducive effect of frequently checking one's understanding.

Discussion

The results underline the significance of frequent use of online self-assessment resources by showing that the frequency with which students responds to online self-assessment questions is a major predictor of their final grade, capstone project success, and quiz success. This is especially significant since it implies that students’ frequent engagement in self-assessment greatly influences students’ comprehension and retention of course material.

The significance of continuous effort over sporadic excellence is suggested by the slightly more substantial impact of the frequency of responding to self-assessment questions relative to the total points obtained from the correct responses. It implies that students who are frequently involved in the self-assessment tend to understand the material better, which results in higher overall grades. This may be explained by the way that learning is scaffolded via frequent engagement with the self-assessment resources, leading to identifying and addressing misconceptions and misunderstandings. Considering the positive correlations between exam scores and self-assessment points (Ćukušić et al., 2014) and between final grades and self-assessment engagement (Thomas et al., 2017), our results supported the scaffolding nature of self-assessment resources.

As for capstone projects, the significance of the frequency of answering self-assessment questions as a positive predictor highlights the role of self-assessments in student success in more complex, long-term assignments. It implies that frequently engaging with self-assessment resources contribute significantly to a student's capstone success. Capstone projects inherently have a student-centered approach to learning, encouraging students to have agency (Baker, 2006; Bell, 2010). Our findings for the predictive nature of self-assessment for capstone project success serve as a supplementary resource when students have the agency while working on capstone projects based on their inquiry.

The frequency of responding to self-assessment questions, the total number of points earned for correct answers, and the amount of time spent using self-assessment tools are the three factors the study finds to predict quiz success. This result demonstrates how complex learning is and the importance of engagement quantity (time and frequency) and quality (correct responses). The assumption that frequent engagement with self-assessment resources is advantageous is further supported by the fact that frequency has the most substantial effect among these criteria. These results elaborate on the significance of self-assessment resources regarding the effect on quiz success, considering quizzes are a tool to promote self-regulation and continuous learning (Blanco & Ginovart, 2012). Also, various studies demonstrate the students’ positive feedback for the existence of quizzes in the learning process (e.g., Chaqmaqchee, 2015; Gamulin & Gamulin, 2012). Therefore, self-assessment resources as supplementary material may increase quiz success and, consequently, learning of factual knowledge presented via quizzes.

Conclusion

The frequency with which students answer self-assessment questions and the total points they gain from correct answers were both key factors that positively predict their final grades. Although both the amount of time spent for answering self-assessment questions and the points they earn were important, the frequency of answering self-assessment questions had a greater impact on the students’ final grades. For the capstone project, how often students answer self-assessment questions were a significant predictor of capstone success. In quizzes, three factors—how often questions are answered, the total points for correct answers, and the amount of time spent—were all important for predicting quiz success. Among these, the frequency of answering self-assessment questions had the highest impact.

Implications and Recommendations

According to our research, using self-assessments frequently is associated with higher academic success. Our research suggested that students’ retention, comprehension of content, and other higher-order cognitive functions can be significantly improved by frequently interacting with educational materials that incorporate self-assessments. This association's fundamental tenet is that effective learning processes need active engagement with the learning materials being studied. Frequent engagement with self-assessment resources can help students better comprehend the material, evaluate their performance and progress, and enhance their learning.

Further, the significance of self-assessment frequency in predicting student grades highlights the role of formative assessments in the teaching and learning process. Formative assessments are incorporated throughout the learning process, unlike summative assessments, which evaluate student learning at the end of an educational process. Because they offer continuous feedback, students are able to track their progress and adjust their learning strategies as necessary (Boekaerts & Corno, 2005; Nicol & Macfarlane-Dick, 2006; Sezgin-Memnun, 2013). To improve their academic achievement and get a more in-depth comprehension of the course material, students need to engage in this continuous feedback loop.

Interestingly, our research revealed that the frequency of engaging with self-assessment resources has a greater influence than the total points obtained on the correctly answered self-assessment questions. This insight raises the possibility that the criteria for evaluating academic achievement might be revised. Academic achievement has been assessed primarily on performance or outcomes. Nonetheless, the significance of the learning process and the students’ frequent efforts were emphasized by this study. There is an emerging perspective that recognizes the value of persistence, resilience, and the willingness to engage with educational materials, regardless of the immediate performance outcomes (Dweck et al., 2014).

A further noteworthy aspect of the research is the diverse influence of different predictors on different assessment types, including quizzes vs capstone projects. This variance shows the possible advantages of personalized learning approaches. Personalized learning takes into account individual student's unique needs and strengths (Akyuz, 2022). By customizing learning experiences to suit individual student's profile, teachers may better address each student's particular needs and leverage their abilities. This approach may result in more meaningful and productive learning opportunities, eventually improving achievement levels.

The importance of formative assessments, the necessity of emphasizing learning processes as well as outcomes, the necessity of active learning engagement, and the possible advantages of personalized approaches to learning are all stressed by this research. These insights can help educators and policymakers create and execute more successful instructional strategies that support students’ development of a deeper, more thorough understanding and appreciation of the learning process in addition to grades.

Educators are encouraged to redesign their courses to include self-assessment resources that promote frequent student engagement instead of just one-time interaction. Teachers can provide students with continuous feedback, supporting the learning process and assisting students in identifying areas for development by integrating self-assessment questions as continuing formative assessments and scaffolding tools throughout the course. It is also recommended that educators and educational institutions emphasize the learning process in addition to outcome-based evaluations, praising and rewarding frequent engagement and effort, a practice that has great implications to educational equity. This emphasis might entail creating courses that adjust to the unique learning needs of each student and providing more frequent and varied assessment options to meet their various needs and preferences. Additionally, LMS and other educational technologies can also be used to track student engagement with self-assessment resources and provide personalized feedback. This would allow teachers to monitor engagement and adapt learning materials to improve the learning process.

Limitations

This study has some limitations. Our investigation focused on specific types of assessments, namely quizzes and capstone projects. Those were selected to investigate the effect of online self-assessment recourses on student success. These two types of assessment represented distinct strategies within educational assessment spectrum, providing a varied yet focused scope for our analysis. Still, it is important to note that this leaves room for further investigation into additional assessment strategies. Future studies might benefit from incorporating online discussions, laboratory assignments, essays, and reflection journals.

Also, our study context was science, with a particular emphasis on geoscience education. Therefore, there exists an opportunity for further research to investigate the effect of online self-assessment resources on student success in other subject areas, such as mathematics and literature. This could yield additional insights into the adaptability and effectiveness of online self-assessment across the educational spectrum.

Table 1.

Model Result for Final Grade.

	Estimate	Standard Error	t-Value	p-Value
(Intercept)	83.077	3.525	23.566	.001
Frequency of answering online self-assessment questions	0.171	0.052	3.309	.001
Total points earned for correctly answered online self-assessment questions	0.110	0.038	2.931	.004
Total time spent on online self-assessment questions	0.000	0.000	0.732	.464

Table 2.

Model Result for Capstone Project Grade.

	Estimate	Standard Error	t-Value	p-Value
(Intercept)	87.004	1.839	47.301	.000
Frequency of answering online self-assessment questions	0.173	0.067	2.567	.011
Total points earned for correctly answered online self-assessment questions	0.098	0.050	1.945	.052
Total time spent on online self-assessment questions	0.000	0.000	0.023	.981

Table 3.

Model Result for Quiz Grade.

	Estimate	Standard Error	t-Value	p-Value
(Intercept)	71.171	10.629	6.696	.021
Frequency of answering online self-assessment questions	0.173	0.052	3.312	.001
Total points earned for correctly answered online self-assessment questions	0.089	0.038	2.334	.020
Total time spent on online self-assessment questions	0.000	0.000	2.125	.034

Footnotes

Authors’ Contribution

ED and ALM contributed to conceptualization and writing—original draft preparation; ED and AYZ to methodology, formal analysis and investigation, and writing—review and editing ; and ALM to supervision.

Consent

All subjects gave informed consent for inclusion before participating in the study.

Data

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval

The study was conducted in accordance with ethical guidelines, and the protocol was approved by the Ethics Committee of Penn State (Study ID: STUDY00010309).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Emre Dinç

Author Biographies

Emre Dinç is a PhD candidate in Learning, Design, and Technology at Penn State. His research interests include STEM education, reasoning, and online learning.

Anna Y. Zhang is a PhD candidate in Educational Psychology, Counseling, and Special Education at Penn State. Her research interests include quantitative modeling in educational setting, feminist theories and educational applications, and Statistics education.

April L. Millet is an associate teaching professor at the John A. Dutton Institute for Teaching and Learning Excellence at Penn State. Her research interests include online learning, instructional strategies, and retrieval practice.

References

Akyuz

(2022). Personalized learning in education. Creative Education, 11(06), 953–978. https://doi.org/10.4236/ce.2020.116069

Alenezi

Wardat

Akour

(2023). The need of integrating digital education in higher education: Challenges and opportunities. Sustainability, 15(6), 4782. https://doi.org/10.3390/su15064782

Arend

B. D.

(2007). Course assessment practices and student learning strategies in online courses. Journal of Asynchronous Learning Networks, 11(4), 3–17. https://doi.org/10.24059/olj.v11i4.1712

Argyriou

Benamar

Nikolajeva

(2022). What to blend? Exploring the relationship between student engagement and academic achievement via a blended learning approach. Psychology Learning & Teaching, 21(2), 126–137. https://doi.org/10.1177/14757257221091512

Baker

R. D.

(2006). Project-based learning, surface energy balance, and establishment of a new undergraduate weather station. Journal of Geoscience Education, 54(3), 320–328. https://doi.org/10.5408/1089-9995-54.3.320

Baker

R. S.

Siemens

(2022). Learning analytics and educational data mining. In Sawyer

R. K.

(Ed.), The Cambridge handbook of the learning sciences (3rd ed., pp. 259–278). Cambridge University Press. https://doi.org/10.1017/9781108888295.016.

Basholli

Baxhaku

Dranidis

Hatziapostolou

(2013). Fair assessment in software engineering capstone projects. Proceedings of the 6th Balkan Conference in Informatics, 244–250. https://doi.org/10.1145/2490257.2490268

Bell

(2010). Project-based learning for the 21st century: Skills for the future. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 83(2), 39–43. https://doi.org/10.1080/00098650903505415

Blanco

Ginovart

(2012). On how Moodle quizzes can contribute to the formative e-assessment of first-year engineering students in mathematics courses. Universities and Knowledge Society Journal, 9(1), 354–370. https://doi.org/10.7238/rusc.v9i1.1277

10.

Boekaerts

Corno

(2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54(2), 199–231. https://doi.org/10.1111/j.1464-0597.2005.00205.x

11.

Boud

Falchikov

(1989). Quantitative studies of student self-assessment in higher education: A critical analysis of findings. Higher Education, 18(5), 529–549. https://doi.org/10.1007/BF00138746

12.

Chaqmaqchee

Z. A.

(2015). Student’s perspective on formative assessment: Quizzes and discussion as ongoing process in higher education. American Scientific Research Journal for Engineering, Technology, and Sciences, 13(1), 160–177. https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/849

13.

Coates

(2016). Assessing student learning outcomes internationally: Insights and frontiers. Assessment & Evaluation in Higher Education, 41(5), 662–676. https://doi.org/10.1080/02602938.2016.1160273

14.

Conrad

Openo

(2018). Assessment strategies for online learning: Engagement and authenticity. AU Press, Athabasca University. https://doi.org/10.15215/aupress/9781771992329.01

15.

Cook

B. R.

Babon

(2017). Active learning through online quizzes: Better learning and less (busy) work. Journal of Geography in Higher Education, 41(1), 24–38. https://doi.org/10.1080/03098265.2016.1185772

16.

Ćukušić

Garača

Jadrić

(2014). Online self-assessment and students’ success in higher education institutions. Computers & Education, 72, 100–109. https://doi.org/10.1016/j.compedu.2013.10.018

17.

DeSouza

Fleming

(2003). A comparison of in-class and online quizzes on student exam performance. Journal of Computing in Higher Education, 14(2), 121–134. https://doi.org/10.1007/BF02940941

18.

Dinç

Millet

A. L.

(2022). Evaluating different assessment types in an online geoscience course. In D. G. Sampson, D. Ifenthaler, & P. Isaías (Eds.), The 19th international conference on cognition and exploratory learning in digital age (CELDA 2022) (pp. 226–232). IADIS.

19.

Dweck

C. S.

Walton

G. M.

Cohen

G. L.

(2014). Academic tenacity: Mindsets and skills that promote long-term learning. Bill & Melinda Gates Foundation. https://eric.ed.gov/?id=ED576649

20.

El-Hashash

(2022). Weekly quizzes reinforce student learning outcomes and performance in biomedical sciences in-course assessments. Open Journal of Educational Research, 2(4), 168–178. https://doi.org/10.31586/ojer.2022.273

21.

El Said

G. R.

(2021). How did the COVID-19 pandemic affect higher education learning experience? An empirical investigation of learners’ academic performance at a university in a developing country. Advances in Human-Computer Interaction, 2021, 1–10. https://doi.org/10.1155/2021/6649524

22.

Enders

Gaschler

Kubik

(2021). Online quizzes with closed questions in formal assessment: How elaborate feedback can promote learning. Psychology Learning & Teaching, 20(1), 91–106. https://doi.org/10.1177/1475725720971205

23.

Fallows

Chandramohan

(2001). Multiple approaches to assessment: Reflections on use of tutor, peer and self-assessment. Teaching in Higher Education, 6(2), 229–246. https://doi.org/10.1080/13562510120045212

24.

Figueroa-Canas

Sancho-Vinuesa

(2020). Early prediction of dropout and final exam performance in an online statistics course. IEEE Revista Iberoamericana de Tecnologias Del Aprendizaje, 15(2), 86–94. https://doi.org/10.1109/RITA.2020.2987727

25.

Fung

C. Y.

S. I.

Perry

E. J.

Garcia

M. B.

(2022). Development of a socioeconomic inclusive assessment framework for online learning in higher education. In Garcia

M. B.

(Ed.), Socioeconomic inclusion during an era of online education (pp. 23–46). IGI Global. https://doi.org/10.4018/978-1-6684-4364-4.ch002.

26.

Gamage

S. H. P. W.

Ayres

J. R.

Behrend

M. B.

Smith

E. J.

(2019). Optimising Moodle quizzes for online assessments. International Journal of STEM Education, 6(1), 1–14. https://doi.org/10.1186/s40594-019-0181-4

27.

Gamulin

(2012). The application of web-based formative quizzes in laboratory teaching in higher education environment. 2012 Proceedings of the 35th International Convention MIPRO, pp. 1383–1388.

28.

(2021). Family conditions and the accessibility of online education: The digital divide and mediating factors. Sustainability, 13(15), 8590. https://doi.org/10.3390/su13158590

29.

Heil

Ifenthaler

(2023). Online assessment in higher education: A systematic review. Online Learning, 27(1), 187–218. https://doi.org/10.24059/olj.v27i1.3398

30.

Ifenthaler

Schumacher

Kuzilek

(2023). Investigating students’ use of self-assessments in higher education using learning analytics. Journal of Computer Assisted Learning, 39(1), 255–268. https://doi.org/10.1111/jcal.12744

31.

Joo

Y. J.

Lim

K. Y.

Lee

S. Y.

(2019). Project-based learning in capstone design courses for engineering students: Factors affecting outcomes. Issues in Educational Research, 29(1), 123–140.

32.

Kokotsaki

Menzies

Wiggins

(2016). Project-based learning: A review of the literature. Improving Schools, 19(3), 267–277. https://doi.org/10.1177/1365480216659733

33.

Kuznetsova

Brockhoff

P. B.

Christensen

R. H. B.

(2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13). https://doi.org/10.18637/jss.v082.i13

34.

Lin

C.-L.

(2018). The development of an instrument to measure the project competences of college students in online project-based learning. Journal of Science Education and Technology, 27(1), 57–69. https://doi.org/10.1007/s10956-017-9708-y

35.

Maleki

R. A.

(2009). Business and industry project-based capstone courses: Selecting projects and assessing learning outcomes. Industry and Higher Education, 23(2), 91–102. https://doi.org/10.5367/000000009788146647

36.

Mathews

A. J.

Wikle

T. A.

(2019). GIS&T pedagogies and instructional challenges in higher education: A survey of educators. Transactions in GIS, 23(5), 892–907. https://doi.org/10.1111/tgis.12534

37.

Mattingly

K. D.

Rice

M. C.

Berge

Z. L.

(2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal, 4(3), 236–247. https://doi.org/10.34105/j.kmel.2012.04.020

38.

Meyer

L. H.

Davidson

McKenzie

Rees

Anderson

Fletcher

Johnston

P. M.

(2010). An investigation of tertiary assessment policy and practice: Alignment and contradictions. Higher Education Quarterly, 64(3), 331–350. https://doi.org/10.1111/j.1468-2273.2010.00459.x

39.

Millet

Dinç

(2023). Synthesizing a conceptual framework for assessment in online education. In L. G. Chova, C. G. Martínez, & J. Lees (Eds.), The proceedings of the 7th international technology, education and development conference (pp. 7893–7899). IATED. https://doi.org/10.21125/inted.2023.2153

40.

Millet

Turcotte

Yan

(2021). Retrieval practice and online learning. In J. Hoffman & P. Blessinger (Eds.), International perspectives in online instruction (Vol. 40, pp. 95–112). Emerald Publishing Limited.

41.

Millet

A. L.

Dinç

Bruhn

H. M.

Feineman

(2022). Relationship of different assessment types in an online interdisciplinary course. In L. G. Chova, A. L. Martínez, & J. Lees (Eds.), The proceeding of the 15th annual international conference of education, research and innovation (pp. 1203–1208). IATED.

42.

Mitchell

Nyamapfene

Roach

Tilley

(2019). Philosophies and pedagogies that shape an integrated engineering programme. Higher Education Pedagogies, 4(1), 180–196. https://doi.org/10.1080/23752696.2018.1507624

43.

Mok

M. M. C.

Lung

C. L.

Cheng

D. P. W.

Cheung

R. H. P.

M. L.

(2006). Self-assessment in higher education: Experience in using a metacognitive approach in five case studies. Assessment & Evaluation in Higher Education, 31(4), 415–433. https://doi.org/10.1080/02602930600679100

44.

Montenegro

Jankowski

N. A.

(2020). A new decade for assessment: Embedding equity into assessment praxis. Urbana, IL: University of Illinois and Indiana University, National Institute for Learning Outcomes Assessment (NILOA).

45.

Mountrakis

Triantakonstantis

(2012). Inquiry-based learning in remote sensing: A space balloon educational experiment. Journal of Geography in Higher Education, 36(3), 385–401. https://doi.org/10.1080/03098265.2011.638707

46.

Nicol

D. J.

Macfarlane-Dick

(2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090

47.

Panadero

Jonsson

(2013). The use of scoring rubrics for formative assessment purposes revisited: A review. Educational Research Review, 9, 129–144. https://doi.org/10.1016/j.edurev.2013.01.002

48.

Panadero

Jonsson

Botella

(2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74–98. https://doi.org/10.1016/j.edurev.2017.08.004

49.

Panadero

Romero

(2014). To rubric or not to rubric? The effects of self-assessment on self-regulation, performance and self-efficacy. Assessment in Education: Principles, Policy & Practice, 21(2), 133–148. https://doi.org/10.1080/0969594X.2013.877872

50.

Papanthymou

Darra

(2019). The contribution of learner self-assessment for improvement of learning and teaching process: A review. Journal of Education and Learning, 8(1), 48–64. https://doi.org/10.5539/jel.v8n1p48

51.

Plotnick

R. E.

Varelas

Fan

(2009). An integrated earth science, astronomy, and physics course for elementary education majors. Journal of Geoscience Education, 57(2), 152–158. https://doi.org/10.5408/1.3544251

52.

Quintana

(2021). Scaffolding inquiry. In Duncan

R. G.

Chinn

C. A.

(Eds.), International handbook of inquiry and learning (1st ed., pp. 174–188). Routledge. https://doi.org/10.4324/9781315685779-12.

53.

Rice

Shannon

L.-J.

(2016). Developing project based learning, integrated courses from two different colleges at an institution of higher education: An overview of the processes, challenges, and lessons learned. Information Systems Education Journal, 14(3), 55–62. http://isedj.org/2016-14/

54.

Salas-Morera

Arauzo-Azofra

García-Hernández

(2012). Analysis of online quizzes as a teaching and assessment tool. Journal of Technology and Science Education, 2(1), 39–45. https://doi.org/10.3926/jotse.30

55.

Sewell

J. P.

Frith

K. H.

Colvin

M. M.

(2010). Online assessment strategies: A primer. MERLOT Journal of Online Learning and Teaching, 6(1), 297–305. http://jolt.merlot.org/vol6no1/sewell_0310.pdf

56.

Sezgin-Memnun

(2013). A comparison of metacognitive awareness levels of future elementary teachers in Turkey and USA. Educational Research and Reviews, 8(6), 277–288. https://doi.org/10.5897/ERR2012.1109

57.

Sitzmann

Ely

Brown

K. G.

Bauer

K. N.

(2010). Self-assessment of knowledge: A cognitive learning or affective measure? Academy of Management Learning & Education, 9(2), 169–191. https://doi.org/10.5465/amle.9.2.zqr169

58.

Tabak

Kyza

E. A.

(2018). Research on scaffolding in the learning sciences. In Fischer

Hmelo-Silver

C. E.

Goldman

S. R.

Reimann

(Eds.), International handbook of the learning sciences (1st ed., pp. 191–200). Routledge. https://doi.org/10.4324/9781315617572-19.

59.

Taras

(2010). Student self-assessment: Processes and consequences. Teaching in Higher Education, 15(2), 199–209. https://doi.org/10.1080/13562511003620027

60.

Terrón-López

M.-J.

García-García

M.-J.

Velasco-Quintana

P.-J.

Ocampo

Vigil Montaño

M.-R.

Gaya-López

M.-C.

(2017). Implementation of a project-based engineering school: Increasing student motivation and relevant learning. European Journal of Engineering Education, 42(6), 618–631. https://doi.org/10.1080/03043797.2016.1209462

61.

Thomas

G. J.

Martin

Pleasants

(2011). Using self- and peer-assessment to enhance students’ future-learning in higher education. Journal of University Teaching and Learning Practice, 8(1), 1–17. https://doi.org/10.53761/1.8.1.5

62.

Thomas

J. A.

Wadsworth

Jin

Clarke

Page

Thunders

(2017). Engagement with online self-tests as a predictor of student success. Higher Education Research & Development, 36(5), 1061–1071. https://doi.org/10.1080/07294360.2016.1263827

63.

Torrijo

F. J.

Garzón-Roca

Cobos

Eguibar

MÁ

(2021). Combining project based learning and cooperative learning strategies in a geotechnical engineering course. Education Sciences, 11(9), 467. https://doi.org/10.3390/educsci11090467

64.

Wan

G. C.

Liu

W. J.

Zhang

Tong

M. S.

(2018). Improvement of capstone project and project-based learning method based on CDIO mode. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 938–943. https://doi.org/10.1109/TALE.2018.8615435

65.

Yan

(2020). Self-assessment in the process of self-regulated learning and its relationship with academic achievement. Assessment & Evaluation in Higher Education, 45, 224–238. https://doi.org/10.1080/02602938.2019.1629390

66.

Zhang

Philbin

S. P.

Huijser

Wang

Jin

(2021). Toward next-generation engineering education: A case study of an engineering capstone project based on BIM technology in MEP systems. Computer Applications in Engineering Education, 30, 146–162. https://doi.org/10.1002/cae.22448

67.

Zimmerman

B. J.

Labuhn

A. S.

(2012). Self-regulation of learning: Process approaches to personal development. In APA Educational psychology handbook, vol 1: Theories, constructs, and critical issues (pp. 399–425). American Psychological Association. https://doi.org/10.1037/13273-014.

The Importance of Frequent Engagement With Self-Assessment Resources in Online Learning: A Hierarchical Linear Modeling Approach

Abstract

Keywords

Introduction

Multifaceted Assessment Strategies for Student Learning

Transformative Assessment: Quizzes

Inquiry-Based Assessment: Capstone Projects

Scaffolding with Self-Assessment Resources

Method

Participants and Setting

Measures

Data Analysis

Results

Discussion

Conclusion

Implications and Recommendations

Limitations

Footnotes

Authors’ Contribution

Consent

Data

Declaration of Conflicting Interests

Ethics Approval

Funding

ORCID iD

Author Biographies

References