2.1 Inquiry-based Learning and Knowledge Integration
According to the National Research Council (NRC), inquiry refers to “the diverse ways scientists study the natural world and propose explanations based on the evidence derived from their work. Inquiry-based learning also refers to activities of students in which they develop knowledge and understanding of scientific ideas and an understanding of how scientists study the natural world.” (Council, 1996). Over the past three decades, inquiry-based learning has become an important pedagogy in science education. The newest curriculum guidelines in the United States and Taiwan promote this approach to assist students in understanding and constructing scientific conceptions (Council, 1996; Ministry of Education, 2014; NGSS Lead States, 2013).
Pedaste et al. (2015) analyzed 32 articles describing the inquiry phase and summarized the five general inquiry phases: orientation, conceptualization, investigation, conclusion, and discussion, as shown in Figure 1. By offering learning tasks and scientific problems, the orientation phase focuses on stimulating interest and curiosity, and it follows up the conceptualization phase that claims concepts of the investigable problems. The investigation phase is where students turn curiosity into action to solve and respond to the research questions or hypotheses. As students have finished experiments and collected data, they should draw an evidence-based conclusion responding to their original research questions or hypotheses in the conclusion phase. Last but not least, the discussion phase occurs when students reflect and communicate on the whole process at the end of a single phase in the cycle.
Previous studies have already pointed out that inquiry-based learning can promote students’ exploring, reasoning, modeling competencies, and academic achievement (Campbell et al., 2015; Chinn & Malhotra, 2002; Sullivan et al., 2017; Ulus & Oner, 2020; White & Frederiksen, 1998; Zimmerman, 2007). The process of scientific inquiry is central to reasoning involving hypothesis, experimental design, evidence evaluation, and drawing inferences (Zimmerman, 2007). Authentic tasks, classified into hands-on inquiry, computer-simulated experimentation, database tasks, evidence evaluation tasks, and verbal designs of research, play an important role in fostering the development of epistemologically scientiﬁc reasoning. In particular, the advantage of simulation tasks is that they allow students to test with complex underlying models that they could not do in reality because of lack of time and equipment (Chinn & Malhotra, 2002).
Knowledge integration (KI) is an approach to assessing complex science reasoning; moreover, in recent years, research has applied the KI framework to measure science inquiry processes, such as the ability to link ideas, distinguish among ideas, and generate arguments (Fang et al., 2016; Linn, 1995; Linn et al., 2006; Liu et al., 2008; Ulus & Oner, 2020). Clark and Linn (2003) mentioned that learners could expand, revise, restructure, and reconnect their ideas through effective teaching. Students’ knowledge integration is dynamic during inquiry-based learning; hence, we design the ILS with knowledge integration tasks to monitor students’ genetic learning.
2.2 The Go-Lab platform with Virtual Labs
Go-Lab is an online learning platform where students can engage in inquiry-based learning in a structured and supportive way (Hovardas et al., 2018). By providing a federation of virtual and remote laboratories and data sets, referred to as “online labs,” the Go-Lab platform offers teachers opportunities to embed these online labs in pedagogically structured and inquiry learning spaces (ILSs) (De Jong et al., 2014). ILSs are online and digital environments where students interact with the learning material, including virtual labs and other multimedia resources, such as text and images (de Jong et al., 2021). In short, ILSs can provide students with a context-based environment and instructional guidance for learning.
With advanced technology, inquiry-based learning can use digital instruments, such as interactive visualizations and virtual laboratories, to assist students in solving problems and understanding complex scientific ideas (De Jong et al., 2013; Linn et al., 2006). Virtual investigations can equal or exceed the influence of physical investigations on conceptual understanding; many studies show no significant differences in conceptual understanding or inquiry competency between physical and virtual experiments (Brinson, 2015; De Jong et al., 2013; Heradio et al., 2016). Many online learning ecosystems, such as Go-Lab, WISE, and PhET, offer students virtual laboratories, which simplify learning by highlighting salient variables, removing confusing details, and modifying time scales to explore and investigate the scientific phenomenon easier (De Jong et al., 1998; Gnesdilow & Puntambekar, 2021; Potkonjak et al., 2016).
The virtual lab is an effective way to elicit students’ understanding of the nature of science (NOS) (De Jong et al., 2013). Knowing something about how scientific knowledge is generated and the limits of scientific knowledge is an essential aim of scientific literacy (American Association for the Advancement of Science, 1993; Ministry of Education, 2018; NGSS Lead States, 2013). Regarding the approaches for learning and teaching the NOS, the implicit approach attempts utilized engagement in inquiry-based learning activities to improve understanding of NOS; on the other hand, the direct approach to learning NOS attempts to utilize certain scientific content, such as history and philosophy of science (Abd-El-Khalick & Lederman, 2000; Schwartz et al., 2004). Develaki (2019) demonstrated an example of supporting NOS understanding by simulation experiments. In summary, we can use the Go-Lab platform to build an ILS embedded with virtual labs for improving students’ understanding of scientific concepts and the nature of science.
2.3 Learning of Mendelian Genetics
Mendelian genetics is an essential concept of heredity in the United States and Taiwan school science. When teaching about heredity, the history and work of Mendel are incorporated (Ministry of Education, 2018; NGSS Lead States, 2013). Taking the Next Generation Science Standards (NGSS), for example, inheritance of traits is one of the disciplinary core ideas. NGSS suggested that Gregor Mendel and the laws he formed can be included when teaching about heredity. Concepts of Mendelian genetics contain dominance, segregation, and independent assortment that relate to the microscopic entities, such as gametes and alleles. Similarly, genetics is one of the learning contents in Taiwanese curriculum guidelines. Regarding Mendelian genetics, seventh-grade students in Taiwan must learn monohybrid crosses, and tenth-grade students are expected to learn dihybrid crosses.
However, due to the abstract and complex links among genetic ideas, students are likely to encounter difficulties when they are asked to describe how the following concepts are related: allele, trait, gamete, and zygote (Allen, 1987; Cho et al., 1985; Stewart, 1988; Stewart, 1982; Tsui & Treagust, 2007). Several studies have attempted to find alternative concepts in genetics and the reasons for the difficulties in learning genetics. One alternative concept is that students do not relate heterozygous and homozygous alleles to the law of dominance. Similarly, students may misunderstand the linked dominant or recessive alleles for two traits (Cho et al., 1985; Stewart, 1983). The other alternative concept concerns dihybrid crosses in which students fail to construct allelic keys and determine the genetic composition of the parental gametes (Browning & Lehman, 1988; Cho et al., 1985; Stewart, 1982).
Mendelian genetics has been widely investigated in genetics problem-solving studies (Browning & Lehman, 1988; Corbett et al., 2010; Slack & Stewart, 1990; Tsui & Treagust, 2003) because students can understand genetics at a deeper level by using existing conceptual knowledge to solve problems or discover the new relationship among concepts (Stewart, 1988; Tsui & Treagust, 2010). Due to genetics experiments would be very difficult to do in the classroom, several studies have focused on computerized learning environments to support the development of students’ understanding, problem-solving, and reasoning of genetics (Buckley et al., 2004; Tsui & Treagust, 2003; Tsui & Treagust, 2010; Tsui & Treagust, 2007). In such learning environments, students could manipulate the links of variables that helped students develop connections among genetic entities; therefore, learning genetics through simulations supported conceptual understanding (Hickey et al., 1999; Tsui & Treagust, 2003; Tsui & Treagust, 2007). Although previous findings suggest that genetics problem-solving learning activities support students’ learning outcomes, there was a noticeable absence of research projects that offer students both evidence evaluation and simulation experiment tasks as scaffolding for learning Mendelian genetics. Thus, this study applies a scaffolding for learning through ILS to help students understand Mendelian genetics’ complex and abstract ideas.
2.4 Technology Acceptance Model
Technology Acceptance Model (TAM) was first proposed by Davis (1989) as a diagnostic tool for evaluating and predicting whether digital device users accept a new information technology system. Numerous studies have used TAM for 20 past years (Ali et al., 2016; Cairns et al., 2021; Dasgupta et al., 2002; Gnidovec et al., 2020; Teo, 2009; Zhai & Shi, 2020); thus, TAM has become well-established as a robust and powerful model for investigating user acceptance. TAM consists of five variables that are described as follows (Davis, 1989; Davis et al., 1989; Venkatesh & Davis, 1996, 2000): (1) External Variables－Any factors that indirectly affect behavior, such as system design characteristics, user’s cognitive style, task characteristics, political factors, etc. (2) Perceived Usefulness－An individual believes that whether a particular system can be used to improve work performance. (3) Perceived Ease of Use－An individuals consider whether they can easily use a particular system with little or no effort. (4) Behavioral Intention－Whether an individual is willing to use a particular system. (5) Actual System Use－Whether an individual uses a particular system. According to TAM, an individual’s behavioral intention to use a system is determined by perceived usefulness and ease of use, and perceived usefulness is also affected by perceived ease of use (Venkatesh & Davis, 2000). In summary, for investigating students’ acceptance of learning in the ILS, we adopted TAM as the framework to design the Go-Lab platform acceptance questionnaire.