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Estimation of individuals’ collaborative problem solving ability in computer-based assessment

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Abstract

In the human-to-human Collaborative Problem Solving (CPS) test, students’ problem-solving process reflects the interdependency among partners. The high interdependency in CPS makes it very sensitive to group composition. For example, the group outcome might be driven by a highly competent group member, so it does not reflect all the individual performances, especially for a low-ability member. As a result, how to effectively assess individuals’ performances has become a challenging issue in educational measurement. This research aims to construct the measurement model to estimate an individual’s collaborative problem-solving ability and correct the impact of partners’ abilities. First, 175 eighth graders’ dyads were divided into six cooperative groups with different levels of problem-solving (PS) ability combinations (i.e., high-high, high-medium, high-low, medium-medium, medium–low, and low-low). Then, they participated in the test of three CPS tasks, and the log data of the dyads were recorded. We applied Multidimensional Item Response Theory (MIRT) measurement models to estimate an individual’s CPS ability and proposed a mean correction method to correct the impact of group composition on individual ability. Results show that (1) the multidimensional IRT model fits the data better than the multidimensional IRT model with the testlet effect; (2) the mean correction method significantly reduced the impact of group composition on obtained individual ability. This study not only successfully increased the validity of individuals’ CPS ability measurement but also provided useful guidelines in educational settings to enhance individuals’ CPS ability and promote an individualized learning environment.

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Data availability

The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

The key project for the 14th five-year plan of Beijing education sciences in 2022 (CDAA22033)

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  1. Meijuan Li

    Present address: Pedagogic Research Institute for Basic Education, Beijing Academy of Educational Sciences, Beijing, China

Authors and Affiliations

  1. Faculty of Psychology, Beijing Normal University, Beijing, China

    Hongyun Liu

  2. Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China

    Hongyun Liu

  3. Psychology, Division of Social Science & Communication, Manhattanville College, Purchase, NY, USA

    Mengfei Cai

  4. Educational Science Research Institute, Hunan University, Changsha, China

    Jianlin Yuan

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Appendix

Appendix

Three human-to-human interaction tasks (Clown, Plant Growth, and Olive Oil) were employed in this study. The following is a detailed description of the coding scheme for the log data, using the "olive oil" task as an example.

1.1 The Olive Oil task

The “Olive Oil” task is a well-defined algorithm problem, which can be completed by two students’ collaboration. As Appendix Fig. 4 shows, the left side is student A’s interface and the right one is student B’s. Two students have jars with different volumes, student A’s is 3L and student B’s is 5L. The task goal is to fill up student B’s jar with 4L olive oil. The transfer pipe can be used to transfer olive oil from student A to B, and the unused olive oil can be put in the bucket. Students A and B need to type texts in the chat windows to communicate and collaborate on this task. The ideal path includes eight steps: 1, Student A filled up 3L olive oil; 2, Student A transfered the 3L olive oil to student B; 3, Student A filled up 3L olive oil again; 4, Student A transfered 2L olive oil to student B and kept 1L olive oil; 5, Student B poured all the olive oil in the 5L jar; 6, Student A transfered the left 1L olive oil to student B; 7, Student A filled up with 3L olive oil again; 8, Student transfered the 3L olive oil to student B so the later has 4L in total. Appendix Fig. 4 shows the instruction and the problem space on two screen tabs in the Olive Oil task.

Fig. 4
figure 4

The screenshot of student A (at the top) and student B (at the bottom) in the Olive Oil Task

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1.2 Log data

Appendix Table 13 shows the original log data of a group of students completing the “Olive Oil” task recorded by a computer. It includes five variables: ID is the sequence number of a group’s behaviors and conversations while completing the task; GroupID is the group number; log_type shows the type of current event of the student; log_content shows the content of current event of the student; role is the student’s mission role. In addition, Table 14 has a detailed introduction of the data types in log_content.

Table 13 Sample log data for the Olive Oil task
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1.3 Coding

The operation behaviors and conversations in the CPS process need to be coded by using the following methods:

1.3.1 Behavior coding

There are two steps in behavior coding. The first one is to delete the meaningless behaviors and keep the meaningful ones. Meaningful behaviors can reflect the progress of CPS mission. For example, students’ filling oil, transferring oil, and pouring oil conducted in the “Olive Oil” task. Meaningless behaviors include the ones that don’t provide any task progress information, such as clicking and dragging the mouse, moving the jar, and turning on and off the transfer pipe in the task.

The second step is to code the meaningful behavior data to describe students’ meaningful behaviors in the CPS process. In order to achieve the goal, the two students in the same group need to collaborate to clearly understand the problem-solving strategy and design a logical problem-solving process. To represent the students’ behavioral status in the CPS process, this type of coding needs to reflect the student’s operation, as well as the cumulated status of all the steps. For example, using the expression formula “A/B 3L/5L_fill/to/trans: 3L = X;5L = Y” to code students’ operation behaviors and the oil volumes in the jars. “A/B” stands for students’ roles; “3L/5L” means the jar volume in operation. Student A uses the 3L jar and student B uses the 5L jar; “fill” means adding oil, “to” means pouring oil, and “trans” means passing oil; “3L = X” is the oil volume in student A’s jar at this moment, and “5L = Y” is the oil volume in student B’s jar at this moment. For example, “A 3L_fill:3L = 3,5L = 0” represents student A added 3L oil at this moment, so his/her jar has 3L oil, while student B’s jar has 0L oil.

1.3.2 Language coding

Language coding has two steps. First, Language coding indicators are determined based on students’ performance, including four dimensions: sharing ideas, negotiating ideas, regulating problem-solving, and maintaining communication (Liu et al., 2015; Hao et al., 2017). 33 coding indicators found by Hao (2017) were expanded to 38 to distinguish students’ language characteristics in CPS process（Table 14). Among the added five indicators, four of them are about sharing ideas, which distinguish the content of sharing ideas (resources, mission progress status), proactivity, and the roles of questioning and responding. The other added indicator is about maintaining communication—students communicating the negative thoughts of giving up. The second step is to use manual coding to achieve language coding. It started with using a coding manual to train all the coding staff. Then 10 sets of data were chosen from each mission, and language contents from different time frames were double-coded. When two codes are inconsistent, it was discussed and finalized. Lastly, 20% of data were chosen and double-coded so the consistency coefficient was calculated. For the three missions, if Kappa coefficient reaches 0.98 on the CPS skills level and reaches 0.84 ~ 0.88 at the student performance (subcategories) level, then they have reached a sufficient consistency coefficient (Cohen, 1960). Once the coding consistency coefficient reaches 0.80, the rest of the data is single-coded .

Table 14 Coding rubic of Language in log data
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1.4 Form structured log data

Through behavior coding and language coding, CPS structured log data is established. Appendix Table 15 presents the data example of structured log data in this task. “Eventtype” is the type of event, in which “action” stands for behaviors, and “chat” stands for language. “Event” represents the structured log data. For example, the fourth event in the group is “C11”, which means student A’s language type at this moment is “to share information related to mission resources with teammate”. The sixth event is “A 3L_fill:3L = 3;5L = 0”, which means at this moment student A is using the 3L jar to fill oil, then he/she has 3L oil, while student B has 0L oil.

Table 15 Sample structured log data for the Olive Oil task
Full size table

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Li, M., Liu, H., Cai, M. et al. Estimation of individuals’ collaborative problem solving ability in computer-based assessment. Educ Inf Technol 29, 483–515 (2024). https://doi.org/10.1007/s10639-023-12271-w

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