- Open Access
An exploration of problem posing-based activities as an assessment tool and as an instructional strategy
© The Author(s) 2015
- Published: 23 June 2015
Problem posing, the generation of questions by learners, has been shown to be an effective instructional strategy for teaching–learning of complex materials in domains such as mathematics. In this paper, we demonstrate the potential of problem posing in two dimensions. Firstly, we present how problem posing can result in unfolding of knowledge and hence how it can be used as an instructional strategy. Then we present another problem posing-based activity as an assessment tool in an Introductory Programming course (CS1).
To explore the potential of problem posing as an instructional strategy, we conducted field studies in the two CS application courses (Data Structures (DS) and Artificial Intelligence (AI)), in which we provided a semi-structured problem posing situation to students. We performed inductive qualitative research and development the questions generated by students using grounded theory-based qualitative data analysis technique. To explore the potential of problem posing as an assessment tool, we conducted a field study in CS1 wherein we employed another problem posing (PP)-based activity in a large class for assessing the learning of computational thinking concepts in an introductory programming course and analysed how performance in traditional assessment tools (quiz score) is related with performance in our non-traditional assessment tool (quality of problems posed during a problem posing activity).
From the studies in DS and AI courses we found that students pose questions and unfold knowledge using seven strategies — Apply, Organize, Probe, Compare, Connect, Vary, and Implement. From the field study performed in the CS1 course we found that the quality of the problems posed (difficulty level) were mostly aligned to the traditional assessment results in the case of novice learners but not in the case of advanced learners.
- Problem posing
- Instructional strategy
- Assessment tool
- Knowledge unfolding
- Computer Science Application courses
Problem posing (PP) refers to the generation of a new problem or a question by learners based on the given situation (Mishra and Iyer 2013). PP has been shown to be useful for identifying knowledge deficit, and opens a way to knowledge exploration. Stoyanova & Ellerton (1996) describe three problem posing situations: free situation, structured situation, and semi-structured situation. Different situations result in the different quality of questions. Variations on these situations can be used to design various PP based activities for different purposes. In this paper we have explored the potential of PP in two dimensions, viz., PP as an instructional strategy, and PP as an assessment tool.
In the first part of this paper, we describe a PP based instructional strategy and report its effect on students’ exploration based learning. We find that the PP based instructional strategy proposed in this research is a way to enable exploration based learning, where students unfold knowledge and explore the course content by posing problems. This exploration based learning inculcates a feeling of ownership of the learning process in the students. The students’ open feedback show that students enjoyed the PP based instruction more than the traditional instructions.
In the second part we describe a field study conducted in CS1 course to explore the assessment potential of PP. We found that students were able to demonstrate their learning through question generation and PP can be used as an assessment tool. We find that all possible computational thinking concepts (Brennan and Resnick 2012) were demonstrated by students generated questions. We also find that different qualitative aspects of the questions help in determining different set of assessment objectives.
In the next section we present motivation and a summary of related studies in the domain of problem posing. Further two sections detail the two explorations, i.e., exploring the potential of PP as an instructional Strategy and as an assessment tool respectively. The last section contains the discussion and conclusion of this research.
Literature suggests that PP involves student in the transformation of knowledge and understanding, engages them in constructing knowledge through various processes, and enables them to generate new knowledge through self-exploration (Ghasempour et al. 2013; Beal and Cohen 2012). The PP activities foster a sense of ownership of learning in students by engaging them in metacognitive strategies (Ghasempour et al. 2013). This motivated us to explore PP as a technique through which students can self-direct their learning.
Designing the PP-based instructional strategy
We employed Design and Development Research (Richey 2014) to develop a QP-based teaching–learning (T-L) strategy to enable student directed learning in classroom settings. Three cycles of Design and Development Research (DDR) has been employed to come up with the current version of the strategy. The developed T-L strategy is known as Student Query Directed Learning (SQDL). The three cycles of DDR are described as follows:
The first DDR cycle
Phase 1—initial instruction phase: The initial instruction phase was used as a semi-structured PP situation (Stoyanova and Ellerton 1996), which was characterized by an initial instruction (seed) by the teacher. The contents of the initial instruction comprise fundamental sub-topics which are essential for the exploration of the complete topic(s). In this paper, we refer to the contents of the initial instruction as “Seed knowledge” or “Seed”. Moreover, this initial instruction explicitly has hints or components, which can encourage exploratory questions among students. The initial instruction was light (less in content), and short (of short time), to ensure that students assimilate (Mayer and Moreno 2003) most of its contents. Students were free to take notes or write questions simultaneously along with attending to the instruction.
Phase 2—problem posing phase: In the second phase, students are asked to pose questions around the content they study in the seed. Students are explicitly told that they can generate questions for two purposes—(a) when they want to clarify any muddy point related to the seed or any previous lecture, and (b) when they want to discover more knowledge related to or based on the contents of the seed instruction. We call this activity of question posing as “think” sub-phase.
After each student has finished posing questions, they are asked to share their questions among each other (“Share” sub-phase). Students are asked to review others’ questions and ensure that the question is not a repetition of their own question. Two students with similar questions were required to disambiguate the question set by removing one of the two similar questions. Students are not asked to discuss the answers with each other, as this would consume enormous amount of time.
Phase 3—addressal phase (instruction next): All the generated questions are collected, and the teacher answered each question one by one. While answering, the teacher is asked to answer “clarification” type questions first (“Clarify” sub-phase) and then answer “exploratory” type questions (“Explore” sub-phase). By “clarification questions,” we refer to all the questions which require reiteration of the content that has been explicitly been taught in the seed or in any other previous lectures in the course. By “exploratory questions,” we refer to the questions which lead to unfolding or construction of new knowledge. Clarification questions are addressed first because they could be the bottle-neck and pre-requisite for understanding the discussions about exploratory questions. During the “clarify” and “explore” sub-phases, the instructor has the liberty not to answer irrelevant and out-of-scope questions.
We did a field study based on this preliminary design in an artificial intelligence (AI) course, and identified the required modifications in the strategy, which led to the revised design of the second DDR cycle.
The second DDR cycle
Taking inputs from the implementation of the preliminary strategy, we modified the strategy by adding a small activity of “summarization” under phase 3. During the “summarization” sub-phase, the teacher summarizes and organizes all the concepts discussed during the “explore” and “clarify” sub-phases. The summarization is essential in order to enable students to make connections between the concepts discussed for a better learning (Fodor and Pylyshyn 1988). We implemented the modified SQDL in a data structures (DS) class. The observations from this implementation suggested further modifications in the SQDL strategy.
The third DDR cycle
The modification done in the SQDL strategy for the third DDR cycle was that an activity of tagging was added to phase 2 (Fig. 1b), i.e., after posing their own questions (“think” sub-phase) and while reviewing others’ questions (“share” sub-phase), students are asked to tag the questions as “low”, “medium”, and “high” according to their perception of the importance of the questions. This ensured that the sharing activity is not merely a way to avoid the redundant questions, but it made students review the questions even deeper. This modification was in line with the requirement of constructionist learning (Papert 1993), which advocates that learning occurs “especially well when the learner is engaged in constructing something for others to see” (Papert 1993, Patten et al. 2006). In the third (and current) version of the SQDL strategy, it is ensured that students construct new knowledge through question posing, and at the same time, they know that their generated question will be reviewed by others and the answer to the questions will be addressed to or discussed with the whole class.
The field study (field study 1) for the first DDR cycle was executed in artificial intelligence (AI) course, whereas the field studies (field studies 2 and 3, respectively) for the second and the third DDR cycles were administered in data structures classes. There were several types of data collected in each field study, but in this paper, the only data that we discuss is the questions generated by students during problem posing phases of field study 1 and field study 2, as the research focused on how much exploration-based learning took place.
In the next sections, we discuss the final version of SQDL and the results obtained from the qualitative analysis of the questions.
Defining SQDL—the final version
We define SQDL as a question posing-based teaching–learning strategy that enables students to regulate their learning by posing questions. Students’ pose questions based on the contents of an initial lecture (“Seed”) and determine which content/sub-topics that are conceptually related to the seed have to be taught next. After the last DDR cycle in the current version of SQDL, a single iteration of SQDL consists of three phases: (1) Initial Instruction Phase, (2) PP Phase, (3) Addressal (or next instruction) phase. Phase 2 is comprised of two sub-phases: (2.1) Think and Tag, (2.2) Share and tag. The third phase is comprised of three sub-phases: (3.1) Clarify, (3.2) Explore, (3.3) Summarize.
In this sub-section we discuss the two implementations of SQDL (field study 1 and field study 2). We delimit our discussion to the collection and analysis of posed questions, with an objective of investigating how much exploration-based learning took place.
Implementations (the PP sessions and data collection)
Artificial intelligence sessions (field study 1):
We administered two PP sessions in a seventh semester engineering classroom of 35 students in an AI course. The first phase or the seed instruction phase was of 15 min. The topic covered in the seed lecture of the first AI session was “Comparison of Attributes of Intelligence in Utility based, Goal Based, and Simple Reflex agents”. The learning objective for the first session of the seed instruction was “By the end of the seed instruction student should be able to identify differences between simple-reflex, goal-based, and utility-based agents, with respect to the level and attributes of intelligence”.
The topic covered in the seed lecture of the second AI session was “The architecture of learning agents”. Learning objective for this session of the seed instruction was “By the end of the seed instruction student should be able to identify the attributes of intelligence present in the learning agents”. The PP phases in the both sessions continued for 10 min. Students wrote their questions on paper slips and submitted to the TAs. Students were explicitly told about the types (clarification and exploratory) of questions that they could prefer to generate. We collected 25 distinct questions in the first session and 23 distinct questions in the second session.
At the end of the AI session, students were asked to write down their feedback to the open-ended question, “How was today’s lecture different, good, and bad from other traditional lectures?” We received responses from 39 students.
Data structure session (field study 2):
Similar to the AI session, we administered a PP session in a 4th semester engineering classroom of 60 students in a DS classroom. The instruction phase was executed for 15 min. Topics covered in the seed lecture were “Node Structure” and “Linking two nodes”. The learning objective of the seed instruction was “By the end of the seed instruction, student should be able to define, declare, construct, and access their own nodes and linkages between nodes using Java.” The PP phase continued for 10–15 min. Students were told to write their questions on paper slips, review the questions from their peers to remove the redundant questions, and submit the final question slips to the TAs. After discarding the irrelevant and remaining redundant questions, we were left with a corpus of 56 distinct questions.
Grounded theory-based qualitative analysis:
We have collected a total of 104 student questions from the two PP sessions. We first conducted an in-depth study of these question statements to find out what strategies students use to pose questions in the given semi-structured PP situation. We employed a grounded theory-based inductive qualitative research methodology. After the completion of the analysis, we found the answer to the more refined research question, “How do students use their prior knowledge/experience, and the knowledge from “seed” to generate a new question?” In this paper, we are not reporting the detailed analysis procedures and output, as it has been communicated for publication elsewhere. The result of the analysis was eight PP strategies that explain how students used prior, and the seed knowledge to come up with new questions.
We further qualitatively analyzed each question to extract the knowledge type of the prior knowledge used to generate the question, knowledge type of the unfolded knowledge for any question, concept (topic/sub-topic) unfolded by any question.
In this paper, we present a descriptive analysis of different PP strategies evident for the question set, the knowledge types of the information requested by the question set, and the amount of knowledge unfolded using PP. The next section contains the analysis results of the study.
Open-ended feedback from students:
To analyze the open-ended responses from all students, we performed a content analysis of the text obtained from their feedback notes. We coded each response to answer three questions: (1) What are the advantages of the PP-based SQDL activity? (2) What are the disadvantages of the activity? (3) Reason behind advantage and/or disadvantages?
Results (PP as an instructional strategy)
PP strategies evolved from the grounded theory-based qualitative analysis of questions
The seed knowledge is employed to create some “known application” from prior knowledge. Explicit identification of prior known application is mandatory in this strategy. Applications are identified either from: 1) the same domain, or 2a) different academic domain, or 2b) real life.
“Creating social network graph, is it possible?”, Here application (“social network graph”) comes from real life experiences.
This strategy aims at unfolding variants of the seed knowledge by organizing multiple instances of the seed concept to obtain some structural arrangement (which comes from prior experience).
“Cyclic list of nodes possible?” Here multiple instances of the concept “node” (from seed knowledge), i.e., large number of nodes are proposed to be organized in a cyclic manner to unfold a variant of the seed (i.e., circular linked list).
Prior knowledge is used as a basis to make a richer inquiry into the seed and used to add more understanding of the seed. Here prior knowledge is NOT the prior known application, as in Apply. Associations between prior knowledge and seed knowledge are performed so as to use prior knowledge as a basis to make a richer enquiry into the seed knowledge.
Example: “address (next) is relative or direct?” Here concepts from prior knowledge (“relative/direct addressing”) has been used to make a richer understanding of the construct “next”, which is a part of seed.
The questioning strategy is to make associations between prior knowledge and seed knowledge such that prior knowledge is compared or contrasted with the concepts in the seed knowledge.
Example: “chain of nodes vs. array?” In this question the prior knowledge (“array”) is contrasted with seed concept (chain of “nodes”).
In this strategy, student associates the seed knowledge to some prior knowledge, from same domain, from other domains, or from real life. Making analogy between some prior knowledge with seed knowledge is included in this strategy. Contrasting or comparing the seed with some prior knowledge does NOT come under this strategy.
Example: “Can we use neural network and fuzzy logic to create an agent?” In this question. the prior concept of “neural network and fuzzy logic” is connected with the context of seed knowledge (“an agent”).
In this strategy, the objective of the question is to modify/ vary the component(s), attribute(s), or part(s) of the seed to unfold the variants of the seed concepts. These questions may or may not give rise to some application of the seed, but applications are NOT explicitly identified.
“In addition to next have previous node?” In this question, instead of having just one pointer/reference to another node, the idea of having two pointer/ reference variables in the node structure, is proposed. In this way a variant of “singly linking” (i.e., a “doubly linking”) is unfolded.
The questions generated using this strategy show that students think about how some operation/procedure, can be performed on the seed knowledge to achieve a goal state related to the seed. It should be noted that prior knowledge, which is in the form of operation/procedure, are explicitly evident from the question statement.
“How to perform inheritance from a node possible to give “multinodes”?” Here the operation inheritance has been explicitly identified, and question is about how to implement that operation on the seed concept (“nodes”).
The analyses revealed that students ask question to clarify their muddy points. All the questions which needed reiteration of the content that has been explicitly been taught in the seed or in any other previous lecture in the course are categorized to follow clarification strategy. Hence clarification questions do not unfold any new knowledge.
“What is the use of ‘this’ method?” The use of “this” operator was explicitly taught in the seed.
Frequency of applications of different PP strategies and related knowledge types
(N = 104)
(N = 104)
Conceptual (0.14), Procedural (0.01)
Conceptual (0.08), Meta-Cog(0.01)
Conceptual (0.05), Procedural (0.02), Factual (0.01)
Conceptual (0.13), Procedural (0.02),
Conceptual (0.07), Factual (0.07)
Conceptual (0.05), Procedural (0.01), meta-cog (0.01)
Conceptual (0.06), Procedural (0.01)
Conceptual (0.05), Factual (0.02), Meta-cog (0.03)
Conceptual (0.05), Factual (0.04), Procedural (0.01)
Conceptual (0.13), Procedural (0.01), Factual (0.01)
Conceptual (0.01), Procedural (0.08), Factual (0.01)
Conceptual (4), Factual (0 + 11), Procedural (2 + 3 + 1), Meta-cog (0 + 1 + 1)
Conceptual (0.02), Procedural (0.03)
The grey nodes represent the concepts which were taught in the instruction phase (i.e., seed concepts), while the red border around a node denotes that there were some clarification questions generated related to that particular concept. The green nodes show the concepts which were unfolded, i.e., they were not taught to students before. The green node with a dotted border is an unfolded concept which is out of the scope of traditional DS syllabus. The concepts denoted by the yellow nodes are the prior knowledge within the domain which was used during PP.
Student’s open-ended feedback
We found that all of the 39 responses suggest that the activity was helpful in learning and creating interest. Students predominantly perceived that the activity was helpful to learning due to the following reasons: (a) The activity helped them to clarify their muddy points and learning basic details. (b) Due to the activity, students came across critical questions. (c) The activity covers all necessary topics. (d) It was better to explore topics more from students point of view. (e) It removed fear and hesitation of participating in the class, and increased active learning. The disadvantage of the activity as perceived by the students was that the activity was very much time-consuming. It would be interesting to study in our future research how much time does the traditional lecture require as compared to the time required by the SQDL approach to cover the same set of topics.
RQ1: How can student-generated questions be used to assess the learning of Computer Programming concepts?
RQ2: How does the quality of question(s) generated by a student relate to the score achieved by him/her in the traditional assessment?
The PP situation of this activity was completely different than that described in the “Problem posing as an instructional strategy” section. Here the “Seed” knowledge was considered to be the 4-week-long (total 12 h) instruction. Moreover, the purpose of this PP activity was to generate questions to assess other students, whereas the purpose of question posing in the SQDL (“Problem posing as an instructional strategy” section) was to clarify or explore knowledge to improve learning.
The PP activity implementation
A team of TAs was assigned to talk to students and motivate them to brainstorm and generate questions that may lead to deeper application of the concepts taught in the class. There were 90 students per lab session, and there was one TA per 10 students. TAs were told to intervene whenever they found any student stuck in the activity, or sitting idle for long time, or busy doing some out-of-context work. It was the responsibility of a senior TA to coordinate with junior TAs to manage all the logistics in the lab session.
Parameters for qualitative analysis of problem posed in programming domain
Creativity of the problem poser
Difficulty of the problem
Programming concepts (can take one or more values)
Low, medium, high
Low, medium, high
Recall, understand, apply, analyze, evaluate, create
Write a program, debugging, predict the output, theoretical (subjective)
Sequence, loops, parallelism (threads), events, conditionals, operators, data (non-array), data arrays
Rubrics for analyzing creativity and difficulty levels
Creativity of the problem poser
The context addressed in the problem is same as textbook programming problems e.g. “Check if a number is prime”. The use of constructs is conventional.
Prior knowledge used in the problem comes from courses experienced in school level.
Attempt of a new context (prior knowledge used in the problem comes from the real-world experiences) and innovative use of constructs.
Difficulty of the problem
Problems with well-understood logic and straightforward solution.
Problems with some amount of logical challenge and do not have a straightforward solution.
Problems which are highly logically challenging and have no straight forward solution.
To answer RQ2, we operationalized the quality of questions using difficulty levels of the questions. Then we explicated the pattern between the difficulty level and the stratified (low, medium, high) scores of the fourth week quiz using stratified attribute tracking diagrams (Majumdar and Iyer 2014).
Results (PP as an instructional strategy)
Learning of programming concepts
Frequencies of questions exhibiting different CTCs
Computational Thinking Concepts (CTC)
Percent of questions requesting any (CTC)
Quality of questions (difficulty levels and creativity)
Difficulty level distribution of questions
Percent of questions of any difficulty level
Creativity level distribution of questions
Percent of questions of any difficulty level
Relation between the traditional assessment score and nontraditional assessment tools
We see that for novices, the higher the score in the quiz, the lesser is the probability of generating a low difficulty question. Probability of generating medium-level difficulty questions by both high- and medium-level quiz performers is evident in both novice and advance cases. Interestingly, high probability of generating low difficulty questions by high pretest performers is evident in the case of advance learners only, this shows that the difficulty level can be used to assess the learning of novices, but not advanced learners.
“…Helpful for doubts”
“…Innovative way of learning…. doubts without being scared”,
“Through today’s activity… I can explore more… can
“find new ideas how far we can go with the subject”,
“…Good way of getting knowledge…”
“It helped in explore topics more from student point of view and hence improved learning…”
SQDL is helpful in student-driven unfolding of course contents which are conceptually related to the seed instruction. However, we do not expect students to ask questions and unfold topics which are conceptually unrelated to the seed concepts. Therefore, in addition to AI and DS, SQDL is suitable for all domains which has a large number of conceptually related topics. The types and distribution PP strategies employed may vary according to the nature of different domains. We believe that there exists potentially interesting research objective to investigate the variations in nature of questions posed across different domains.
The second PP situation (PPE activity) was designed to explore the potential of problem posing as an assessment tool. We found that PP can be used to assess the learning of computational thinking concepts by students in the CS1 course. In the PPE activity, students generate questions and they also provide solutions/answers to them. This ensures that the concepts which are required to answer a question are understood by the students. We aggregated all these concepts that emerged from the generated questions and determined the frequency distribution of various concepts learned by the students. It should be noted that we did not assess the learning of any individual student on the topics around which (s)he has not generated questions. Though, PPE can be used to assess the learning of different concepts by the class, as a whole. We also attempted to study the relation of “understanding of programming” (operationalized by the quiz scores) with question quality (operationalized by the “difficulty level” of the questions). We found that for novice learners, the higher the score in the quiz, the lesser was the probability of generating low difficulty question. Interestingly, in the case of advance learners, we found a high probability of generating low difficulty questions by high quiz performers. This shows that the difficulty level can be used to assess the learning of novices, but not of advanced learners. Moreover, it is also possible that in addition to “understanding of programming” the “difficulty level” of the generated question might be affected by other factors. Although the results in the paper show some relation between the traditional assessment scores and PPE-based assessment, we do not claim any statistical correlation.
With content analysis of questions for the concepts that any question relates to, PPE can be used in other domains for assessing the conceptual understanding. As far as the difficulty level and other quality parameters are concerned, different domains may need different rubrics for analysis. The use of PPE as an assessment tool shows that different qualitative aspects of questions can reveal a lot about different aspects of learning, and other cognitive and affective parameters. For example, the account of creativity shows how much students are able to relate the concepts to their prior (real-world or academic) experiences. More of these aspects are to be identified to make PP useful for assessing a wide range of objectives.
- Akay, H, & Boz, N. (2009). Prospective teachers’ views about problem-posing activities. Procedia-Social and Behavioral Sciences, 1(1), 1192–1198.View ArticleGoogle Scholar
- Anderson, LW, Krathwohl, DR, & Bloom, BS. (2001). A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives. Allyn and bacon.Google Scholar
- Arikan, EE, Unal, H, & Ozdemir, AS. (2012). Comparative analysis of problem posing ability between the Anatolian high school students and the public high school students located in Bagcilar District of Istanbul. Procedia-Social and Behavioral Sciences, 46, 926–930.View ArticleGoogle Scholar
- Beal, CR, & Cohen, PR. (2012). Teach Ourselves: Technology to Support Problem Posing in the STEM Classroom. Creative Education, 3(4).Google Scholar
- Beck, IL. (1997). Questioning the author: an approach for enhancing student engagement with text. Order department, international reading association, 800 Barksdale road, PO Box 8139, Newark, DE 19714-8139.Google Scholar
- Brennan, K, & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada.Google Scholar
- Cai, J, & Hwang, S. (2002). Generalized and generative thinking in US and Chinese students’ mathematical problem solving and problem posing. The Journal of Mathematical Behavior, 21(4), 401–421.View ArticleGoogle Scholar
- Cai, J, Moyer, JC, Wang, N, Hwang, S, Nie, B, & Garber, T. (2013). Mathematical problem posing as a measure of curricular effect on students’ learning. Educational Studies in Mathematics, 1, 13.Google Scholar
- Cankoy, O, & Darbaz, S. (2010). Effect of a problem posing based problem solving instruction on understanding problem. Hacettepe University Journal of Education, 38, 11–24.Google Scholar
- Chi, MT, Siler, SA, Jeong, H, Yamauchi, T, & Hausmann, RG. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471–533.View ArticleGoogle Scholar
- Çildir, S, & Sezen, N. (2011). A study on the evaluation of problem posing skills in terms of academic success. Procedia-Social and Behavioral Sciences, 15, 2494–2499.View ArticleGoogle Scholar
- Corbett, A, & Mostow, J. (2008). Automating comprehension questions: lessons from a reading tutor. Workshop on the question generation shared task and evaluation challenge. Arlington, VA: NSF.Google Scholar
- Dillon, JT. (1982). Problem finding and solving. The journal of creative behavior, 16(2), 97–111.View ArticleGoogle Scholar
- Dillon, JT. (1990). The practice of questioning. London: Routledge.Google Scholar
- DiPaolo, RE, Graesser, AC, Hacker, DJ, & White, HA. (2004). Hints in human and computer tutoring. The design of instruction and evaluation: Affordances of using media and technology, 155.Google Scholar
- Dougiamas, M, & Taylor, P. (2003). Moodle: using learning communities to create an open source course management system”. In World conference on educational multimedia, hypermedia and telecommunications, Vol. 1 (pp. 171–178).Google Scholar
- Edelson, DC, Gordin, DN, & Pea, RD. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8(3-4), 391–450.View ArticleGoogle Scholar
- English, LD. (1998). Children’s problem posing within formal and informal contexts. Journal for Research in Mathematics Education, 83, 106.Google Scholar
- Fodor, JA, & Pylyshyn, ZW. (1988). Connectionism and cognitive architecture: a critical analysis. Cognition, 28(1), 3–71.View ArticleGoogle Scholar
- Ghasempour, Z, Bakar, MN, & Jahanshahloo, GR. (2013). Innovation in teaching and learning through problem posing tasks and metacognitive strategies. Int. J. Ped. Inn, 1(1), 57–66.View ArticleGoogle Scholar
- Good, TL, Slavings, RL, Harel, KH, & Emerson, H. (1987). Student passivity: a study of question asking in K-12 classrooms. Sociology of Education, 181, 199.Google Scholar
- Graesser, AC, & Person, NK. (1994). Question asking during tutoring. American Educational Research Journal, 31(1), 104–137.View ArticleGoogle Scholar
- Graesser, AC, McNamara, D, & VanLehn, K. (2005). Scaffolding deep comprehension strategies ThroughPoint&query, AutoTutor, and iSTART. Educational psychologist, 40, 225-234.60, 181-199.Google Scholar
- Graesser, A, Otero, J, Corbett, A, Flickinger, D, Joshi, A, & Vanderwende, L. (2008). Guidelines for question generation shared task evaluation campaigns. In The Question Generation Shared Task & Evaluation Challenge Workshop Report, University of Memphis. http://www.cs.columbia.edu/~sstoyanchev/qg/
- Gubareva, AE. (1992). Teaching by posing questions. Biochemical Education, 20(4), 226–227.View ArticleGoogle Scholar
- Hacker, DJ, Dunlosky, JE, & Graesser, AC. (1998). Metacognition in educational theory and practice. Lawrence Erlbaum Associates Publishers.Google Scholar
- Kar, T, Özdemir, E, Ipek, AS, & Albayrak, M. (2010). The relation between the problem posing and problem solving skills of prospective elementary mathematics teachers. Procedia-Social and Behavioral Sciences, 2(2), 1577–1583.View ArticleGoogle Scholar
- Lavy, I, & Bershadsky, I. (2003). Problem posing via “what if not?” strategy in solid geometry—a case study. The Journal of Mathematical Behavior, 22(4), 369–387.View ArticleGoogle Scholar
- Lavy, I, & Shriki, A. (2010). Engaging in problem posing activities in a dynamic geometry setting and the development of prospective teachers’ mathematical knowledge. The Journal of Mathematical Behavior, 29(1), 11–24.View ArticleGoogle Scholar
- Leacock, C, & Chodorow, M. (2003). Crater: scoring of short-answer questions. Computers and the Humanities, 37, 389–405.View ArticleGoogle Scholar
- Lee, H, & Cho, Y. (2007). Factors affecting problem finding depending on degree of structure of problem situation. The Journal of Educational Research, 101(2), 113–123.View ArticleGoogle Scholar
- Majumdar, R, & Iyer, S. (2014). Using stratified attribute tracking (SAT) diagrams for learning analytics.View ArticleGoogle Scholar
- Mayer, RE, & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.View ArticleGoogle Scholar
- McComas, WF, & Abraham, L. (2004). Asking more effective questions. Rossier School of Education.Google Scholar
- McDowell, C, Werner, L, Bullock, H, & Fernald, J. (2002). The effects of pair-programming on performance in an introductory programming course. In ACM SIGCSE Bulletin (Vol. 34, No. 1, pp. 38-42). ACM.Google Scholar
- Mestre, JP. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. Journal of Applied Developmental Psychology, 23(1), 9–50.View ArticleGoogle Scholar
- Mishra, S, & Iyer, S. (2013). Problem posing exercises (PPE): an instructional strategy for learning of complex material in introductory programming courses. In Technology for education (T4E), 2013 IEEE fifth international conference on (pp. 151-158). IEEE.Google Scholar
- Papert, S. (1993). The children’s machine: rethinking school in the age of the computer. Basic books.Google Scholar
- Patten, B, Arnedillo Sánchez, I, & Tangney, B. (2006). Designing collaborative, constructionist and contextual applications for handheld devices. Computers & Education, 46(3), 294–308.View ArticleGoogle Scholar
- Pintér, K. (2012). On teaching mathematical problem-solving and problem posing.Google Scholar
- Profetto-McGrath, J, Bulmer Smith, K, Day, RA, & Yonge, O. (2004). The questioning skills of tutors and students in a context based baccalaureate nursing program. Nurse Education Today, 24(5), 363–372.View ArticleGoogle Scholar
- Richey, RC, & Klein, JD. (2014). Design and development research. In Handbook of research on educational communications and technology (pp. 141–150). New York: Springer.View ArticleGoogle Scholar
- Scardamalia, M, & Bereiter, C. (1992). Text-based and knowledge based questioning by children. Cognition and Instruction, 9(3), 177–199.View ArticleGoogle Scholar
- Sengül, S, & Katranci, Y. (2012). Problem solving and problem posing skills of prospective mathematics teachers about the ‘sets’ subject. Procedia-Social and Behavioral Sciences, 69, 1650–1655.View ArticleGoogle Scholar
- Silver, EA. (1997). Fostering creativity through instruction rich in mathematical problem solving and problem posing. ZDM, 29(3), 75–80.View ArticleGoogle Scholar
- Silver, EA, Mamona-Downs, J, Leung, SS, & Kenney, PA. (1996). Posing mathematical problems: an exploratory study. Journal for Research in Mathematics Education, 293, 309.Google Scholar
- Stoyanova, E, & Ellerton, NF. (1996). A framework for research into students’ problem posing in school mathematics. Technology in mathematics education. Melbourne: Mathematics Education Research Group of Australia.Google Scholar
- Toluk-Uçar, Z. (2009). Developing pre-service teachers understanding of fractions through problem posing. Teaching and Teacher Education, 25(1), 166–175.View ArticleGoogle Scholar
- Vanderwende, L. (2008). The importance of being important. workshop on the question generation shared task and evaluation challenge. Arlington, VA: NSF.Google Scholar
- Wallerstein, N. (1987). Problem-posing education: Freire’s method for transformation. Freire for the classroom: A sourcebook for liberatory teaching, 33, 44.Google Scholar
- Williams, L, Kessler, RR, Cunningham, W, & Jeffries, R. (2000). Strengthening the case for pair programming. IEEE software, (4), 19–25.Google Scholar
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