This paper examines assessment methodologies in project-based learning (PjBL) in post-secondary design education, with a specific focus on collaborative teamwork environments. It explores the vital role of universities in fostering key 21st-century capabilities, including critical thinking, problem-solving, and interdisciplinary collaboration. The paper examines the transition from traditional assessments to more comprehensive and inclusive approaches, emphasizing the significance of tailoring learning experiences to individual needs. It also explores the potential of using advanced technological and data-driven methods, such as learning analytics and artificial intelligence, to enhance assessment methods in collaborative project-based learning. Additionally, I examine ethical considerations related to these methods.

Keywords: Project-based learning (PjBL), collaborative learning, learning analytics (LA), artificial intelligence in education (AiED), teamwork assessment, ethics

Effective teamwork abilities have become increasingly essential for students to learn because they reflect the interconnected and dynamic character of the professional environment (Brosens et al., 2023; Cortázar et al., 2022; Vartiainen et al., 2022). More importantly, many cognitive tasks are increasingly relegated to computer programs and algorithms, while people are expected to take on more complex tasks requiring critical thinking and collaboration with others (Levy & Murnane, 2004). Collaboration with others has the potential to integrate diverse perspectives, therefore encouraging problem-solving through shared knowledge and enabling blending complementary skills. Within various professional domains, projects are often complex and require the integration of multiple areas of expertise.

Thus, students skilled at collaborating in multidisciplinary teams tend to be more well-prepared for their future careers (Wickliff, 1997; see also Rohm et al., 2021). This competency is particularly relevant in environments that value innovation and creativity, where collaborative efforts often lead to ground-breaking solutions and advancements. While many of the competencies required in the future are unknown, teamwork and collaboration have been noted as two of the most sought-after skills that employers look for in graduates (Britton et al., 2017; Cortázar et al., 2022; Planas-Lladó et al., 2021; Strom & Strom, 2011). Collaborative competencies have also been linked to overall student success, with studies showing a 17% greater achievement rate than individual work (Llanos Mosquera et al., 2021). By cultivating teamwork skills, universities are enhancing individual student competencies and cultivating a flexible, collaborative workforce capable of addressing the complex challenges of the modern world.

To effectively teach collaborative competencies, PjBL has emerged as a vital method to encourage teamwork (Jalinus et al., 2020). However, assessing students in collaborative PjBL environments is intrinsically challenging, especially when considering measurements not exclusive to a single domain and including a diverse group of students (Koh et al., 2018).

In online education, particularly in large courses, the assessment process plays a crucial role in the engagement between the instructor and the students, often serving as the sole point of interaction. This dialogue is not only transactional; it establishes the basis of a learning environment defined by engagement, care, and growth. Nevertheless, in a large online course, a considerable number of students might pose difficulties for a single instructor to manage and uphold the standard of assessment. This is intensified in a PjBL setting where the traditional transmission view of feedback and assessment (Nicol & Macfarlane-Dick, 2006) substantially increases the instructor's workload. This is because the instructor must allocate time and effort to assess and offer relevant feedback for both individual contributions and the overall output of the team to meet the requirement for individualized input. Depending on the project topic, instructors must guide students to help them navigate complex tasks (Heo et al., 2010). This can be rather time-consuming, mainly when teams are at varying stages of comprehension.

Furthermore, consolidating the comments of several reviewers in a sizable class can create additional work for the instructor (Darvishi et al., 2022). Several online tools [1] are available to assist with peer assessment. However, these technologies require instructors to learn extra software that may not necessarily prioritize sound user interface principles.While the role of human insight in the assessment process cannot be overstated, as it cultivates a feeling of connection and personalization essential for successful learning, it is worthwhile to explore technological approaches, such as learning analytics and artificial intelligence (AI), to augment the instructor's ability to provide timely and meaningful feedback and formative assessment along the learning journey of individual students and student teams.

[1] Pear Assessment - https://www.peardeck.com/products/pear-assessment, iPeer - https://ipeer.ctlt.ubc.ca/

This literature review explores the key components of PjBL; including its core principles, traditional assessments, the integration of advanced technological tools for assessment, and their ethical concerns. PjBL represents a fundamental shift from conventional lecture-based teaching to a model in which students take charge of their learning process (Parra Pennefather, 2022), focusing on critical thinking, problem-solving, and collaboration. Blumenfeld et al. (1991) and Kurt and Akoglu (2023) argue that PjBL prioritizes practical problem-solving and interdisciplinary teamwork, reflecting the complexity of the professional world. This pedagogy, as discussed by scholars such as Bell (2010) and Rohm et al. (2021), involves students participating in complex projects that require input from several fields of study, therefore fostering an integrated skill set.

The collaborative nature of project work requires a nuanced approach to assessment, as Chu et al. (2017) and Fiorini et al. (2022) emphasize. Traditional assessment techniques frequently face challenges in accurately capturing both the creative solutions produced by the teams and the process involved in the production. Peer assessment, although improving student accountability (Kao, 2013) and increasing the variety of input (Cho & MacArthur, 2011; Patchan & Schunn, 2015), may be flawed because of students' limited skills to assess their peers (Cevik, 2015; Gurbanov, 2016). Moreover, Rotsaert et al. (2018) point out that the interpersonal relationships between students can impact how they assess each other and how each student views the value of peer assessment overall.

The introduction of analytical and data-driven technologies, such as learning analytics (LA) and artificial intelligence (AI), holds great promise for enhancing assessment practices in PjBL. As Bulut et al. (2023) and Gašević et al. (2015, 2016, 2022) discuss, these technologies and tools foster a more customized learning experience and may offer a comprehensive understanding of student performance. In addition, multimodal learning analytics (MMLA) can add a new dimension to assessing collaboration in online settings by measuring various types of student interactions in controlled scenarios, as investigated by Järvelä et al. (2023) and Giannakos et al. (2022). However, integrating big data, learning analytics, and artificial intelligence in education presents significant ethical concerns. Issues of data privacy, student consent, algorithmic bias, and ongoing surveillance are urgent, as highlighted by Gomez et al. (2022) and Ruipérez-Valiente et al. (2022). Therefore, it is vital to develop ethical principles and theoretical guidelines to ensure the appropriate and fair use of AI and LA in education (Hwang et al., 2020; Wise & Shaffer, 2015).

Several case studies demonstrate the practical use and inherent complexity of technologically supported assessment (Conde et al., 2016; Khosravi et al., 2022; Knight et al., 2020; Martinez-Maldonado et al., 2019). The works discussed in this literature review emphasize the significance of maintaining a balance between technological progress and the essential human element, emphasizing the intricate nature and potential of integrating technology into the assessment process fairly and transparently.

Project-Based Learning (PjBL), considered an alternative to lecture-based and teacher-led instruction (C.-H. Chen & Yang, 2019), focuses on students engaging in complex real-world projects that can result in a final artifact, report, or team presentation. This pedagogy empowers students to assume responsibility for their learning and cultivate essential 21st-century skills such as critical thinking, problem-solving, and teamwork (Kurt & Akoglu, 2023). PjBL operates on the core principle that students are the main catalysts of their learning journey and not simply receivers of information. By actively participating in the process of inquiry, such as selecting a project topic that is personally relevant or exploring potential solutions to a problem (Frank et al., 2003), students develop a sense of ownership, autonomy, and a deeper connection with the subject matter. Blumenfeld et al. (1991) define PjBL as having two main components: a problem that can be solved and concrete deliverables resulting from the project.

The projects, central to PjBL, are focused on real-world problems that mirror the complexities and interdisciplinary nature of the professional world and society that the students will likely encounter after graduation (Bell, 2010; Hughes & Jones, 2011; Parra Pennefather, 2022; Rohm et al., 2021). These projects are not simply assignments but rather complex elements of work centred on real-life problems (Hadyaoui & Cheniti-Belcadhi, 2023). PjBL bridges the gap between theoretical knowledge and practical application and promotes a broader perspective in problem-solving, encouraging students to collaborate across topic areas and disciplines. Interdisciplinary collaboration is a cornerstone of PjBL (Hmelo-Silver et al., 2008; Jones et al., 1997), reflecting the interconnectedness and demands of the modern workforce and society. Teamwork skills and equipping students with the capabilities to work and engage in interpersonal relationships with people across diverse backgrounds are critical 21st-century skills (Ahonen & Kankaanranta, 2015; Bell, 2010; Kauppi et al., 2020). Meta-analyses of computer-supported collaborative learning (CSCL) have demonstrated that students who engage in online and face-to-face teamwork exhibit better academic performance than those who study independently and show higher scores on knowledge assessments and improved problem-solving abilities (J. Chen et al., 2018; Raes et al., 2015; Slof et al., 2021).

Moreover, PjBL emphasizes the learning process by placing great value on the student's learning journey, including inquiry, application, and reflection. Helle et al. (2006) categorize PjBL into two distinct aspects. One side involves vertical learning, which focuses on acquiring subject-matter knowledge, while the other involves horizontal learning, which encompasses cognitive abilities such as project management. Binkley et al. (2012) developed a similar framework for categorizing skills in their study, dividing 21st-century skills into four groups. The categories encompass cognitive processes such as problem-solving, tools such as collaboration and information literacy, and a fourth category that pertains to a student's understanding of cultural and global issues and their role as citizens. These two studies on categorizing skills into distinct categories might provide valuable insights for creating holistic assessments in PjBL.

Current Assessment Practices in PjBL

Project-based pedagogy necessitates rethinking assessment methodologies, shifting from primarily assessing outcomes to focusing on formative assessment for the individual learner and the group, emphasizing the learning process. Teamwork, however, can be challenging to quantify since it consists of multiple interconnected behaviours and attitudes (Britton et al., 2017), and it is also difficult to define. For this paper, I define teamwork as individual students working in small groups toward a common objective by creating a prototype or artifact that aims to solve a real-world project challenge. This encompasses the process and individual students' contributions toward the collective effort (Hughes & Jones, 2011).

Assessing Teamwork

Despite the numerous benefits of student collaboration, assessment in PjBL proves challenging. The collaborative nature of the projects requires assessments to strike a balance between recognizing the teams' collective output and the individual student's contributions. Most research on teamwork has primarily concentrated on the group's performance, with individual assessments conducted separately. Therefore, assessing personal skills and competencies related to the group activity has been challenging. The work by Griffin and Care (2015) addresses this issue by showing how to quantify group efforts. Chu et al. (2017) discuss the challenges of acquiring relevant data to effectively identify the contributions of individual students in collaborative projects.

Additionally, cognitive skills such as creativity, critical thinking, and collaboration, which are integral to PjBL, are inherently challenging to measure as they do not lend themselves easily to traditional forms of assessment. This is further exacerbated in an online setting, where the instructor is not privy to the intricacies of the collaborative process and has little control over deteriorating team dynamics (Conrad & Openo, 2018). Therefore, innovative methods that can accurately assess these competencies are needed.

According to Conde et al. (2016), assessment in PjBL is often focused on the final artifact developed by the group, obscuring the learning process, and making it challenging to evaluate each student's contribution and overall performance. Moreover, the tendency of some team members to contribute less effort to a project when working in a group compared to working independently further complicates the issue. Researchers refer to this as "social loafing" (Lin et al., 2021, p. 583) or the free rider syndrome (Haataja et al., 2022; Koh et al., 2018). Students identified unequal contributions in teamwork as a significant factor that negatively impacted their learning experience (Lin et al., 2021; Wilson et al., 2018).

With the move to online delivery for face-to-face (F2F) classes during and since the COVID-19 pandemic, an increase in social loafing has been reported by Wildman et al. (2021). To deal with the issue of social loafing, Roberts and McInnerney (2007) suggest two solutions that tie in with group assessment. The first approach requires the instructor to create specific assignments, such as a team contract, that define the roles and responsibilities of each team member to ensure accountability among students. The second approach includes using "peer pressure openly and unashamedly" (p. 261). While their research holds promise, using the term peer pressure in the context of education is problematic.

Over the years, I have provided equal grades to all team members for the project portion of my project-based course [2]. Particularly in dysfunctional teams, this has consistently appeared unfair, and I have struggled to encourage collaboration while simultaneously recognizing the contributions of individual students. The literature presents conflicting views about the suitable course of action in the case of team grading. Roberts and McInnerney (2007) argue that "assigning group grades without attempting to distinguish between individual members" (p.264) is both unfair and detrimental to the learning process. In contrast, proponents of project-based learning, such as Webb (1995), highlight that the objective is to assess the collective performance of the group and not focus too much on individual contributions.

Measuring Team Competence with Digital Traces. In a case study from Spain, Conde et al. (2016) utilized the Comprehensive Training Model of the Teamwork Competence (CTMTC) tool to measure teamwork competence in a collaborative learning environment. The team analyzed the digital traces generated by individual students and groups as they engaged in the project development. The CTMTC tool is structured around several stages, adapted from the International Project Management Association (IPMA). To facilitate these processes, the researchers collected data from several online platforms, including the course LMS, wikis, forums, and the social media app WhatsApp. Managing individual learning requires ongoing monitoring. In a large class, this can be challenging. To collect all the necessary data, the researchers used LA tools and rubrics to access and analyze data about students' interactions with each other, their engagement in forum discussions, and both individual participation and group dynamics. While this case study is promising in some respects, the students indicated some teamwork-related challenges in a follow-up survey. In particular, the CTMTC tool did not address topics like communication and motivation. Approximately 44% of students reported difficulties with the communication tools, expressing a preference for instant messaging applications over LMS forums for documenting real-time conversations among team members. This mirrors my teaching experience, with students objecting to using the forum in the course LMS and often suggesting alternate options that align with their experience outside the university.

Multitouch Tabletop for Concept Mapping. While the following example by Martinez-Maldonado et al. (2019) predominantly concerns co-located teams, some parallels can be drawn to an online setting. Participants were involved in a task that combined individual and collaborative concept mapping using a multitouch tabletop. Initially, each participant developed a concept map draft using a computer. Following this, teams of three collaborated to design a shared concept map. While tracking their touch inputs, the tabletop interface allowed the teams to incorporate their individual maps, add new components, and change the layout. Additionally, students were recorded with video cameras, providing extra data on how group members interacted and contributed to the shared concept map.

This collected data was then used to devise a system that can automatically analyze and make sense of various types of user interactions, including touch (via the multitouch tabletop), speech (communication between the team members), and visual cues (gestures). The study's goal was to recognize and understand the patterns that emerge during the collaboration process. For example, the system might show how often a student speaks, how students interact with each other, how students engage with the concept map on the tabletop, and how these types of interactions might provide a better understanding of the collaborative process. The validity of this study largely depends on how accurately the data collected reflects actual user behaviour and team interactions. There is a strong possibility that the awareness of being observed influences the behaviour of individual students and the group dynamics. The authors raised concerns about the issue of student surveillance, asking what can and should be recorded in educational settings.

Self – and Peer Assessment.

In most project-based courses, a main assessment component asks students to evaluate their learning journey and that of their peers (Alt & Raichel, 2022; Freeman & McKenzie, 2002; Kao, 2013; Phielix et al., 2010; Planas-Lladó et al., 2021) to achieve a more equitable measurement. While this provides students with a reflective practice that promotes deep learning, it also ensures that students are not solely fixated on the final product or artifact but are equally invested in the learning that occurs along the way.

In the context of collaborative project-based learning, Pellegrino, in his seminal work on measuring what matters (2013), highlights the significance of assessment as a tool that should foster learning and, therefore, be carefully designed and integrated into a course rather than being a separate or final stage of the learning process. He advocates for embedding assessment within the learning activities, enabling continuous feedback and adjustments in teaching and learning. Research by Darvishi et al. (2022) shows that including students in peer assessment has several benefits for both the assessors and the assessees. This is supported by findings from a meta-analysis on peer evaluations (Li et al., 2010) that suggest that students providing feedback to their peers show improved understanding of the learning materials. Additional benefits include the development of a stronger sense of accountability among students (Kao, 2013), improved writing skills, and learning to provide constructive feedback (Lundstrom & Baker, 2009). Assessees, on the other hand, have the advantage of receiving prompt and personalized feedback from peers with diverse viewpoints (Cho & MacArthur, 2011; Patchan & Schunn, 2015), which is seen as less authoritative and more conducive to a mutual exchange of ideas and negotiation (Topping, 1998, 2003), which is crucial in effective teamwork. However, peer reviews for assessment purposes in PjBL can be inadequate as students generally lack the necessary skills to evaluate their classmates appropriately (Cevik, 2015; Gurbanov, 2016), resulting in assessments that may not accurately represent individual student's contributions. The outcome has been mixed in my experience with peer assessments in project-based courses. On the one hand, it can strengthen a team's cohesiveness, while in a dysfunctional team, peer feedback, particularly when given anonymously, can exacerbate feelings of distrust and even psychological unsafety. These points are echoed by research by Harris and Brown (2013) and Vanderhoven et al. (2015), as described by Rotsaert et al. (2018).
E-Portfolios

E-portfolios, another widely used method of assessment in PjBL (Frank et al., 2003; Gulbahar & Tinmaz, 2006), and in design education, function as a repository for individual learning experiences. Barrett (2007) defines e-portfolios as a collection of works and reflections demonstrating a student's growth over time. They allow learners to reflect on their experiences, document their skills, and curate a collection of their best work. According to Garthwait and Verrill (2003), the main goal of e-portfolios is "…to keep students focused on learning rather than on individual projects or products – e-portfolios are part of the learning process, not a result of it" (p. 23).

Further challenges arise in assessing collaborative design education, in addition to the assessment of collaboration already mentioned. Design education encourages creativity in perception and expression by allowing, even encouraging, multiple solutions to a given problem or assignment (Arkun Kocadere & Ozgen, 2012; De Sausmarez, 1964). As such, design is an ill-defined discipline that makes assessing students objectively, individually or in teams, challenging. The creative solutions in team projects are as diverse as the students themselves, and the objectivity of assessment criteria and the instructor who does the assessment is challenging (Fiorini et al., 2022). Instructors are therefore tasked with assessing not only the final artifact but also the individual student's learning process – a challenging undertaking that requires a nuanced and time-intensive approach.

In the following sections, I will explore the potential of data-driven assessment methods, including learning analytics (LA) and artificial intelligence (AI), to enhance our understanding of individual student and group performance.

[2] BET 350: Customer Experience Design. This undergraduate course is part of the Minor in Entrepreneurship and is available in the fall, winter, and spring semesters. The average number of students enrolled each semester is 200. University of Waterloo. https://uwaterloo.ca/conrad-school-entrepreneurship-business/bet-courses#BET350

With the proliferation of learning analytics (LA), educational data mining (EDM) and artificial intelligence in education (AiED), large amounts of data about students' learning behaviours, preferences, and overall performance are constantly being collected (Gomez et al., 2022), typically without the explicit consent of the students. This raises profound ethical concerns. Ruipérez-Valiente et al. (2022) note that analyzing trace data alone might yield limited inferences on learning since recorded data do not provide a comprehensive picture of the entire learning process, often neglecting contextual information. Trace data in an educational context refers to students' digital actions and interactions within a learning platform or environment. These could include timestamps of activities, navigation paths, forum posts, assignment submissions, library visits, or performance in quizzes. This raises concerns about privacy violations, especially when sensitive information is involved. So, it is crucial that students are fully aware of the data collected about them, the nature of its use, and the option to withdraw at any time without penalty.

Moreover, for-profit companies with opaque data privacy policies privately own many of the of the technologies and platforms we use in Canadian universities. Without strict regulations and guidelines, there is a potential danger that student data might be misused and utilized for purposes other than intended, such as targeted advertising or profiling, resulting in discrimination or unfair treatment. Decisions, including assessments, based on poorly analyzed data or algorithms with inherent biases can adversely affect students' educational experiences and opportunities.

Lastly, the constant surveillance of students in technology-rich learning environments is hugely problematic. It also leaves out the rich contextual information that only human observation and interaction can provide. Life events are impacting a student's learning journey. Just during the fall 2023 semester, some of my students lost close family members, became sick, had their hearts broken, or dealt with severe mental health concerns. The data collected via the LMS did not reflect any of these events. This is why the role of a human teacher is so relevant, perhaps more than ever. As educational institutions increasingly rely on data-driven approaches, it is imperative to consider how this trend will shape educational practices and the student experience over time. Balancing the benefits of big data in enhancing educational outcomes on one side with the ethical implications of its collection and use on the other side remains a complex and ongoing challenge.

Wise and Shaffer (2015) strongly argue for the need to incorporate a theoretical framework into data collection in an educational setting as the volume of data increases. This is essential, especially when utilizing learning analytics and AI. Selecting relevant variables and interpreting the findings can be compromised without a theoretical foundation.

Adding a theoretical framework guarantees that the process of gathering and examining data is purposeful and in line with educational objectives, rather than being driven solely by the capabilities of technology. This alignment helps generate more meaningful and contextually relevant data regarding student learning. In addition, a theoretical foundation ensures that the conclusions drawn are not just data-driven but are also pedagogically sound and beneficial for educational outcomes. This approach leads to more responsible and ethical use of student data, safeguarding against biases and misinterpretations that could negatively impact students' academic experiences. Instead of using the term' data mining', Wise and Sheffer propose' data geology' or 'data archeology' as appropriate metaphors for analyzing large masses of data in educational settings to include the "underlying conceptual relationships and the situational context" (p.6).

This paper has examined the evolving assessment landscape in PjBL, specifically in online collaborative settings. This investigation aims to acknowledge the importance of cultivating teamwork skills and collaborative competencies in students while embracing the individual student's learning journey. The paper supports the shift from traditional lecture-based instruction to more student-centred, interactive, and project-based approaches.

However, assessing teamwork skills and competencies presents unique challenges, particularly in large online courses where the human element is invaluable but difficult to scale. By integrating advanced technologies such as learning analytics and artificial intelligence into the assessment processes in PjBL, a more customized learning experience may be possible, including how we assess individual students along their learning journey. However, integrating learning analytics and artificial intelligence in online courses also introduces ethical concerns, such as data privacy, student surveillance, algorithmic bias, and the potential for dehumanizing the learning experience. The paper underscores the need to design fair, transparent assessments that accurately measure individual contributions and group efforts.

Lastly, the personalization of assessment and feedback is where LA and AI's potential is most evident and relevant to my research purpose. Given the increasing size of my classes and the varying rates of progress and skill levels among students, there is a significant opportunity to implement AI-driven feedback mechanisms and leverage real-time data from LA systems (Bulut et al., 2023), to create a personalized learning experience on a large scale.

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