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1. Detecting Trait versus Performance Student Behavioral Patterns Using Discriminative Non-Negative Matrix Factorization

   Mirzaei, M. ; Sahebi, S. ; Brusilovsky, P. ( January 2020 , The Thirty-Third International Flairs Conference)

   Recent studies have shown that students follow stable behavioral patterns while learning in online educational systems. These behavioral patterns can further be used to group the students into different clusters. However, as these clusters include both high- and low-performance students, the relation between the behavioral patterns and student performance is yet to be clarified. In this work, we study the relationship between students’ learning behaviors and their performance, in a self-organized online learning system that allows them to freely practice with various problems and worked examples. We represent each student’s behavior as a vector of highsupport sequential micro-patterns. Then, we discover both the prevalent behavioral patterns in each group and the shared patterns across groups using discriminative non-negative matrix factorization. Our experiments show that we can successfully detect such common and specific patterns in students’ behavior that can be further interpreted into student learning behavior trait patterns and performance patterns.
2. Cognitive agency and computer-based tasks

   Riling, M. ( January 2018 , Proceedings of the 40th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   As computer-focused policies and trends become more popular in schools, more students access math curriculum online. While computer-based programs may be responsive to some student input, their algorithmic basis can make it more difficult for them to be prepared for divergent student thinking, especially in comparison to a teacher. Consider programs that assess student work by judging how well it matches pre-set answers. Unless designed and enacted in classrooms with care, computer-based curriculum materials might encourage students to think about mathematics in pre-determined ways. How do students approach the process of mathematics while using online materials, especially in terms of engaging in original thought? Drawing on Pickering’s (1995) dance of agency and Sinclair’s (2001) conception of students as path-finders or track-takers, I define two modes of mathematical behavior: trail-taking and bushwhacking. While trail-taking, students follow an established approach, often relying on Pickering’s (1995) disciplinary agency, wherein the mathematics “leads [them] through a series of manipulations” (p. 115). The series of manipulations can be seen as a trail that a student may choose to follow. Bushwhacking, on the other hand, refers to actions a student takes of their own invention. It is possible that, unknown to the student, these actions havemore »been taken before by others. In bushwhacking, the student possesses agency, which Pickering (1995) describes as active (rather than passive) and as hallmarked by “choice and discretion” (p. 117). In this study, students worked in several dynamic geometric environments (DGEs) during a geometry lesson about the midline theorem. The lesson was originally recorded as part of a larger study designing mathematically captivating lessons. Students accessed both problems and online addresses for corresponding DGEs via a printed packet. Students interacted with the DGEs on individual laptops, but were seated in groups of three or four. Passages of group conversations in which students transitioned between trail-taking and bushwhacking were selected for closer analysis, which involved identifying evidence of each mode and highlighting the curricular or social forces that may have contributed to shifts between modes. Of particular interest were episodes in which students asked one another to share results, which led to students reconsidering previously set approaches, and episodes in which students interacted with DGEs containing a relatively high proportion of drag-able components, which corresponded to some students working in bushwhacking mode, spontaneously suggesting and revising approaches for manipulating the DGE (e.g., “unless you make this parallel to the bottom, but I don’t think you... yes you can.”). Both types of episodes were found in multiple groups’ conversations. Further analysis of student interactions with tasks, especially with varying levels of student control and sharing, could serve to inform future computer-based task design aimed to encourage students to productively engage in bushwhacking while problem-solving.« less
3. “GPA Patterns of Black Mechanical Engineering Students.”

   Manning, Jessica ; Mobley, Catherine ; Orr, Marisa ; Brawner, Catherine ; Brent, Rebecca ; Tidwell, Michael L. ( January 2022 , Proceedings of the 2022 Annual Conference of the American Society for Engineering Education. https://peer.asee.org/41089)

   In recent years, research has associated grade point average (GPA) with a variety of student outcomes during their undergraduate careers. The studies link higher GPAs to students being more likely to graduate in their major, while lower GPAs have been linked to students switching majors or leaving the institution. Further research, which focuses on how Black female and male students remain successful in different engineering degrees, is necessary to identify the underlying elements contributing to their entrance into and exit from engineering disciplines. This quantitative examination of trends among the GPAs of Black women and men is part of a larger NSF-funded mixed-methods study that includes in-depth student interviews of Black students who persisted in and switched from ME. In this quantitative paper, we examine the GPA patterns of Black students in Mechanical Engineering (ME). Students who have ever enrolled in ME have four potential, mutually exclusive, outcomes: 1) they can persist for 12 semesters without graduating; 2) they can graduate in ME within 12 semesters; 3) they can switch to another major; or 4) they can leave school. In this research, we identify the most common GPA patterns associated with graduated ME students. We hypothesize a relationship between distinctmore »GPA patterns and whether a student persists in ME, graduates in ME, switches away from ME, or leaves the institution altogether. This quantitative investigation uses the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) to collect the cumulative GPA of ME students at each term. We use a functional cluster analysis approach to group similar patterns. First, a function is fit to each student record. Then a cluster analysis is conducted on the function parameters to identify natural groupings in the data. Once students are grouped according to their GPA profile, we examine the other characteristics and outcomes of the group. We present a visual quantitative analysis of the patterns in the GPAs of Black women and men who enroll in ME. Clustering analysis suggests that first-time-in-college (FTIC) Black female students in ME who graduated have a higher proportion of students in the higher GPA clusters than the proportion of FTIC Black male students who graduated in ME. A higher proportion of the male student population is clustered in the lower GPA cluster groups as compared to women in the lower GPA cluster groups. A higher proportion of students who graduated are in the higher GPA clusters than the proportion of graduated students in the lower GPA clusters.« less
4. STUDENT PRESENTATION-BASED TEACHING TO PROVIDE TRANSACTIONAL ANALYSIS TRAINING TO PRODUCE EMOTIONALLY INTELLIGENT ENGINEERING GRADUATE STUDENTS

   Tyagi, Pawan ; Addo, Sampson ( January 2022 , EDUlearn 2022, 14th annual International Conference on Education and New Learning Technologies)

   In the 21st Century, it becomes of utmost importance for the educator and learner to be mindful of the evolution and application of factors that govern the mental state. Many studies revealed that the success of a professional is strongly dependent on their emotion management skills to manage themselves and associated responsibilities in a demanding environment. Emotionally intelligent professionals are also able to handle challenging situations involving other people. These days many industries, research establishments, and universities that hire graduate students conduct specialized training to enhance their soft skills, mainly interpersonal skills, to make their employees perform at their highest potential. One can maximize the gain from soft skills if they are well aware of the state of human psychology developed in the form of emotional intelligence and positive intelligence. In the last two decades, the concept of emotional intelligence was created by professional personality coaching groups. These trainings are heavily attended by professionals engaged in marketing and organization leaders to enhance their capability in the workplace. However, emotional intelligence is mainly about being aware of the mental state and maintaining control of one's actions during various mental states, such as anger, happiness, sadness, remorse, etc. Aspiring graduate students inmore »science and technology generally lack formal training in understanding human behavior and traits that can adversely impact their ability to perform and innovate at the highest level. This paper focuses on training graduate students about the popular and practical transactional analysis science and assessing their competence in utilizing this knowledge to decipher their own and other people's behavior. Transactional analysis was taught to students via Student presentation-based effective teaching (SPET) methodology. Under this approach, graduate students enrolled in the MECH 500 Class were provided a set of questions to answer by self-reading of the recommended textbook "I am OK You are OK by Thomas Harris." Each student individually answered the assignment questions and then worked in the group to prepare a group presentation for the in-class discussion. Three group discussions were conducted to present different views about the four types of transactions and underlying human traits. Before transactional analysis training, students were also trained in Positive intelligence psychology tools for a similar objective. After the discussion, students were surveyed about the depth of their understanding. Students also reflected their views on the utility of transactional analysis with respect to positive intelligence. More than 75% of students mention that they gain high competency in understanding, defining, and utilizing transactional analysis. This study presents insights for positively impacting graduate students' mindsets as they pursue an unpredicted course of research that can sometimes become very challenging.« less
5. Peer preferences and characteristics of same-group and cross-group social interactions among autistic and non-autistic adolescents

   https://doi.org/10.1177/13623613211005918  

   Chen, Yu-Lun ; Senande, Laura L ; Thorsen, Michael ; Patten, Kristie ( October 2021 , Autism)

   Social interaction is inherently bidirectional, but research on autistic peer interactions often frames communication as unidirectional and in isolation from the peer context. This study investigated natural peer interactions among six autistic and six non-autistic adolescents in an inclusive school club over 5 months (14 45-min sessions in total) to examine the students’ peer preferences in real-world social interactions and how the preferences changed over time. We further examined whether social behavior characteristics differ between student and peer neurotype combinations. Findings showed that autistic students were more likely to interact with autistic peers then non-autistic peers. In both autistic and non-autistic students, the likelihood of interacting with a same-neurotype peer increased over time. Autistic and non-autistic students’ within-neurotype social interactions were more likely to reflect relational than functional purposes, be characterized as sharing thoughts and experiences rather than requesting help or objects, and be highly reciprocal, as compared with cross-neurotype interactions. These peer preferences and patterns of social interactions were not found among student-peer dyads with the same genders. These findings suggest that peer interaction is determined by more than just a student’s autism diagnosis, but by a combination of student and peer neurotypes. Lay abstract Autistic students often experience challengesmore »in peer interactions, especially for young adolescents who are navigating the increased social expectations in secondary education. Previous research on the peer interactions of autistic adolescents mainly compared the social behaviors of autistic and non-autistic students and overlooked the peers in the social context. However, recent research has shown that the social challenges faced by autistic may not be solely contributed by their social differences, but a mismatch in the social communication styles between autistic and non-autistic people. As such, this study aimed to investigate the student-and-peer match in real-world peer interactions between six autistic and six non-autistic adolescents in an inclusive school club. We examined the odds of autistic and non-autistic students interacting with either an autistic peer, a non-autistic peer, or multiple peers, and the results showed that autistic students were more likely to interact with autistic peers then non-autistic peers. This preference for same-group peer interactions strengthened over the 5-month school club in both autistic and non-autistic students. We further found that same-group peer interactions, in both autistic and non-autistic students, were more likely to convey a social interest rather than a functional purpose or need, be sharing thoughts, experiences, or items rather than requesting help or objects, and be highly reciprocal than cross-group social behaviors. Collectively, our findings support that peer interaction outcomes may be determined by the match between the group memberships of the student and their peers, either autistic or non-autistic, rather than the student’s autism diagnosis.« less